text:
@article{FCN,
abstract = {Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build 'fully convolutional' networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional networks achieve improved segmentation of PASCAL VOC (30{\%} relative improvement to 67.2{\%} mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.},
archivePrefix = {arXiv},
arxivId = {1411.4038},
author = {Shelhamer, Evan and Long, Jonathan and Darrell, Trevor},
doi = {10.1109/TPAMI.2016.2572683},
eprint = {1411.4038},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Long, Shelhamer, Darrell - 2014 - Fully Convolutional Networks for Semantic Segmentation.pdf:pdf},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Convolutional Networks,Deep Learning,Semantic Segmentation,Transfer Learning},
mendeley-groups = {ComputerVision},
month = {nov},
number = {4},
pages = {640--651},
title = {{Fully Convolutional Networks for Semantic Segmentation}},
url = {http://arxiv.org/abs/1411.4038},
volume = {39},
year = {2017}
}

@inproceedings{MaskRCNN,
abstract = {We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available.},
archivePrefix = {arXiv},
arxivId = {1703.06870},
author = {He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
doi = {10.1109/ICCV.2017.322},
eprint = {1703.06870},
isbn = {9781538610329},
issn = {15505499},
month = {dec},
pages = {2980--2988},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Mask R-CNN}},
volume = {2017-October},
year = {2017}
}
@article{Ulku2019,
abstract = {Semantic segmentation is the pixel-wise labelling of an image. Since the problem is defined at the pixel level, determining image class labels only is not acceptable, but localising them at the original image pixel resolution is necessary. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high level and hierarchical image features; excessive numbers of deep learning-based 2D semantic segmentation approaches have been proposed within the last decade. In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. We started with an analysis of the public image sets and leaderboards for 2D semantic segmantation, with an overview of the techniques employed in performance evaluation. In examining the evolution of the field, we chronologically categorised the approaches into three main periods, namely pre-and early deep learning era, the fully convolutional era, and the post-FCN era. We technically analysed the solutions put forward in terms of solving the fundamental problems of the field, such as fine-grained localisation and scale invariance. Before drawing our conclusions, we present a table of methods from all mentioned eras, with a brief summary of each approach that explains their contribution to the field. We conclude the survey by discussing the current challenges of the field and to what extent they have been solved.},
archivePrefix = {arXiv},
arxivId = {1912.10230},
author = {Ulku, Irem and Akagunduz, Erdem},
eprint = {1912.10230},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Ulku, Akagunduz - 2019 - A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images.pdf:pdf},
month = {dec},
title = {{A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images}},
url = {http://arxiv.org/abs/1912.10230},
year = {2019}
}
@article{OpenAI2019,
abstract = {We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik's cube with a humanoid robot hand, which involves both control and state estimation problems. Videos summarizing our results are available: https://openai.com/blog/solving-rubiks-cube/},
archivePrefix = {arXiv},
arxivId = {1910.07113},
author = {OpenAI and Akkaya, Ilge and Andrychowicz, Marcin and Chociej, Maciek and Litwin, Mateusz and McGrew, Bob and Petron, Arthur and Paino, Alex and Plappert, Matthias and Powell, Glenn and Ribas, Raphael and Schneider, Jonas and Tezak, Nikolas and Tworek, Jerry and Welinder, Peter and Weng, Lilian and Yuan, Qiming and Zaremba, Wojciech and Zhang, Lei},
eprint = {1910.07113},
month = {oct},
title = {{Solving Rubik's Cube with a Robot Hand}},
url = {http://arxiv.org/abs/1910.07113},
year = {2019}
}
@article{James2017,
abstract = {End-to-end control for robot manipulation and grasping is emerging as an attractive alternative to traditional pipelined approaches. However, end-to-end methods tend to either be slow to train, exhibit little or no generalisability, or lack the ability to accomplish long-horizon or multi-stage tasks. In this paper, we show how two simple techniques can lead to end-to-end (image to velocity) execution of a multi-stage task, which is analogous to a simple tidying routine, without having seen a single real image. This involves locating, reaching for, and grasping a cube, then locating a basket and dropping the cube inside. To achieve this, robot trajectories are computed in a simulator, to collect a series of control velocities which accomplish the task. Then, a CNN is trained to map observed images to velocities, using domain randomisation to enable generalisation to real world images. Results show that we are able to successfully accomplish the task in the real world with the ability to generalise to novel environments, including those with dynamic lighting conditions, distractor objects, and moving objects, including the basket itself. We believe our approach to be simple, highly scalable, and capable of learning long-horizon tasks that have until now not been shown with the state-of-the-art in end-to-end robot control.},
archivePrefix = {arXiv},
arxivId = {1707.02267},
author = {James, Stephen and Davison, Andrew J. and Johns, Edward},
eprint = {1707.02267},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/James, Davison, Johns - 2017 - Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task.pdf:pdf},
month = {jul},
title = {{Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task}},
url = {http://arxiv.org/abs/1707.02267},
year = {2017}
}
@article{Rodola,
author = {Rodol, Emanuele and Harada, Tatsuya and Kanezaki, Asako and A, Emanuele Rodol},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Rodol et al. - Unknown - グラフマッチング学習を用いたrgb-D画像からの物体検出.pdf:pdf},
keywords = {3d shape,gradient descent method,graph matching,keypoint matching,optimization},
number = {2},
title = {{グラフマッチング学習を用いたrgb-D画像からの物体検出}},
volume = {1}
}
@article{Xue2019a,
abstract = {Cervical intraepithelial neoplasia (CIN) grade of histopathology images is a crucial indicator in cervical biopsy results. Accurate CIN grading of epithelium regions helps pathologists with precancerous lesion diagnosis and treatment planning. Although an automated CIN grading system has been desired, supervised training of such a system would require a large amount of expert annotations, which are expensive and time-consuming to collect. In this paper, we investigate the CIN grade classification problem on segmented epithelium patches. We propose to use conditional Generative Adversarial Networks (cGANs) to expand the limited training dataset, by synthesizing realistic cervical histopathology images. While the synthetic images are visually appealing, they are not guaranteed to contain meaningful features for data augmentation. To tackle this issue, we propose a synthetic-image filtering mechanism based on the divergence in feature space between generated images and class centroids in order to control the feature quality of selected synthetic images for data augmentation. Our models are evaluated on a cervical histopathology image dataset with a limited number of patch-level CIN grade annotations. Extensive experimental results show a significant improvement of classification accuracy from 66.3{\%} to 71.7{\%} using the same ResNet18 baseline classifier after leveraging our cGAN generated images with feature-based filtering, which demonstrates the effectiveness of our models.},
archivePrefix = {arXiv},
arxivId = {1907.10655},
author = {Xue, Yuan and Zhou, Qianying and Ye, Jiarong and Long, L. Rodney and Antani, Sameer and Cornwell, Carl and Xue, Zhiyun and Huang, Xiaolei},
eprint = {1907.10655},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification{\_}Xue et al.pdf:pdf},
pages = {1--9},
title = {{Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification}},
url = {http://arxiv.org/abs/1907.10655},
year = {2019}
}
@article{Gaier2019b,
abstract = {Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network compared to its architecture? In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task. We propose a search method for neural network architectures that can already perform a task without any explicit weight training. To evaluate these networks, we populate the connections with a single shared weight parameter sampled from a uniform random distribution, and measure the expected performance. We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On a supervised learning domain, we find network architectures that achieve much higher than chance accuracy on MNIST using random weights. Interactive version of this paper at https://weightagnostic.github.io/},
archivePrefix = {arXiv},
arxivId = {1906.04358},
author = {Gaier, Adam and Ha, David},
eprint = {1906.04358},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Gaier, Ha - 2019 - Weight Agnostic Neural Networks.pdf:pdf},
pages = {1--16},
title = {{Weight Agnostic Neural Networks}},
url = {http://arxiv.org/abs/1906.04358},
year = {2019}
}
@article{Kim2019a,
abstract = {We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier. Unlike previous attention-based methods which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes. Moreover, our new AdaLIN (Adaptive Layer-Instance Normalization) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending on datasets. Experimental results show the superiority of the proposed method compared to the existing state-of-the-art models with a fixed network architecture and hyper-parameters.},
archivePrefix = {arXiv},
arxivId = {1907.10830},
author = {Kim, Junho and Kim, Minjae and Kang, Hyeonwoo and Lee, Kwanghee},
eprint = {1907.10830},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Kim et al. - 2019 - U-GAT-IT Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image.pdf:pdf},
title = {{U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation}},
url = {http://arxiv.org/abs/1907.10830},
year = {2019}
}
@article{Chiu2019a,
abstract = {Predicting the future is an important aspect for decision-making in robotics or autonomous driving systems, which heavily rely upon visual scene understanding. While prior work attempts to predict future video pixels, anticipate activities or forecast future scene semantic segments from segmentation of the preceding frames, methods that predict future semantic segmentation solely from the previous frame RGB data in a single end-to-end trainable model do not exist. In this paper, we propose a temporal encoder-decoder network architecture that encodes RGB frames from the past and decodes the future semantic segmentation. The network is coupled with a new knowledge distillation training framework specifically for the forecasting task. Our method, only seeing preceding video frames, implicitly models the scene segments while simultaneously accounting for the object dynamics to infer the future scene semantic segments. Our results on Cityscapes outperform the baseline and current state-of-the-art methods. Code is available at https://github.com/eddyhkchiu/segmenting{\_}the{\_}future/.},
archivePrefix = {arXiv},
arxivId = {1904.10666},
author = {Chiu, Hsu-kuang and Adeli, Ehsan and Niebles, Juan Carlos},
eprint = {1904.10666},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Segmenting the Future{\_}Chiu, Adeli, Niebles.pdf:pdf},
title = {{Segmenting the Future}},
url = {http://arxiv.org/abs/1904.10666},
year = {2019}
}
@article{Babin2019a,
abstract = {The stable and repeatable grasping of objects lying on a flat hard surface is addressed in this paper. A physical model of an object lying on a flat surface and its interaction with the environment and with a gripper is proposed. The important parameters governing the interaction are obtained. From this model, a grasping procedure is established and a robotic gripper is modified in order to grant the ability to pick up large thin objects lying on smooth hard surfaces. The procedure is implemented to demonstrate its repeatability on a chosen set of objects. It is shown that by sensing the force applied on the object and by taking advantage of the nature of the contact provided by the passive joint of the modified finger, a wide range of previously not directly graspable objects are made graspable via the application of a general approach. The experimental results reported clearly show the benefits of the simple force sensing implemented in the gripper as well as of the use of passive joints when interacting with very stiff environments. The proposed approach, while simple, yields a repeatable solution to a complex manipulation problem.},
author = {Babin, Vincent and St-Onge, David and Gosselin, Cl{\'{e}}ment},
doi = {10.1016/j.rcim.2018.06.002},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Stable and repeatable grasping of flat objects on hard surfaces using passive and epicyclic mechanisms{\_}Babin, St-Onge, Gosselin.pdf:pdf},
issn = {07365845},
journal = {Robotics and Computer-Integrated Manufacturing},
keywords = {Constrained grasping,Epicyclic mechanism,Gripper,Passive finger,Repeated grasping,Robot grasping},
number = {June 2018},
pages = {1--10},
publisher = {Elsevier Ltd},
title = {{Stable and repeatable grasping of flat objects on hard surfaces using passive and epicyclic mechanisms}},
url = {https://doi.org/10.1016/j.rcim.2018.06.002},
volume = {55},
year = {2019}
}
@article{Touvron2019a,
abstract = {Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between the typical size of the objects seen by the classifier at train and test time. We experimentally validate that, for a target test resolution, using a lower train resolution offers better classification at test time. We then propose a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ. It involves only a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images. For instance, we obtain 77.1{\%} top-1 accuracy on ImageNet with a ResNet-50 trained on 128x128 images, and 79.8{\%} with one trained on 224x224 image. In addition, if we use extra training data we get 82.5{\%} with the ResNet-50 train with 224x224 images. Conversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86.4{\%} (top-5: 98.0{\%}) (single-crop). To the best of our knowledge this is the highest ImageNet single-crop, top-1 and top-5 accuracy to date.},
archivePrefix = {arXiv},
arxivId = {1906.06423},
author = {Touvron, Hugo and Vedaldi, Andrea and Douze, Matthijs and J{\'{e}}gou, Herv{\'{e}}},
eprint = {1906.06423},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Fixing the train-test resolution discrepancy{\_}Touvron et al.pdf:pdf},
title = {{Fixing the train-test resolution discrepancy}},
url = {http://arxiv.org/abs/1906.06423},
year = {2019}
}
@article{Vecerik2018,
abstract = {Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumbersome to implement, and must be tailored to each individual problem by a qualified engineer. Within the learning community there is a long history of insertion research, but existing approaches are typically either too sample-inefficient to run on real robots, or assume access to high-level object features, e.g. socket pose. In this paper we show that relatively minor modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a small number of human demonstrations, allows the robot to quickly learn to solve these tasks efficiently and robustly. Our approach requires no modeling or simulation, no parameterized search or alignment behaviors, no vision system aside from raw images, and no reward shaping. We evaluate our approach on a narrow-clearance peg-insertion task and a deformable clip-insertion task, both of which include variability in the socket position. Our results show that these tasks can be solved reliably on the real robot in less than 10 minutes of interaction time, and that the resulting policies are robust to variance in the socket position and orientation.},
archivePrefix = {arXiv},
arxivId = {1810.01531},
author = {Vecerik, Mel and Sushkov, Oleg and Barker, David and Roth{\"{o}}rl, Thomas and Hester, Todd and Scholz, Jon},
eprint = {1810.01531},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning{\_}Vecerik et al.pdf:pdf},
title = {{A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1810.01531},
year = {2018}
}
@article{Cubuk2018a,
abstract = {Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5{\%} which is 0.4{\%} better than the previous record of 83.1{\%}. On CIFAR-10, we achieve an error rate of 1.5{\%}, which is 0.6{\%} better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars.},
archivePrefix = {arXiv},
arxivId = {1805.09501},
author = {Cubuk, Ekin D. and Zoph, Barret and Mane, Dandelion and Vasudevan, Vijay and Le, Quoc V.},
eprint = {1805.09501},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}AutoAugment Learning Augmentation Policies from Data{\_}Cubuk et al.pdf:pdf},
number = {Section 3},
title = {{AutoAugment: Learning Augmentation Policies from Data}},
url = {http://arxiv.org/abs/1805.09501},
year = {2018}
}
@article{Kanezaki2018a,
abstract = {We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10- and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves the state-of-the-art performance on an object pose estimation dataset. The code is available on https://github.com/kanezaki/rotationnet},
archivePrefix = {arXiv},
arxivId = {arXiv:1603.06208v4},
author = {Kanezaki, Asako and Matsushita, Yasuyuki and Nishida, Yoshifumi},
doi = {10.1109/CVPR.2018.00526},
eprint = {arXiv:1603.06208v4},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}RotationNet Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints{\_}Kanezaki, Matsushita, Nis.pdf:pdf},
isbn = {9781538664209},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {5010--5019},
title = {{RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints}},
year = {2018}
}
@article{Siam2018a,
abstract = {Video object segmentation is an essential task in robot manipulation to facilitate grasping and learning affordances. Incremental learning is important for robotics in unstructured environments, since the total number of objects and their variations can be intractable. Inspired by the children learning process, human robot interaction (HRI) can be utilized to teach robots about the world guided by humans similar to how children learn from a parent or a teacher. A human teacher can show potential objects of interest to the robot, which is able to self adapt to the teaching signal without providing manual segmentation labels. We propose a novel teacher-student learning paradigm to teach robots about their surrounding environment. A two-stream motion and appearance "teacher" network provides pseudo-labels to adapt an appearance "student" network. The student network is able to segment the newly learned objects in other scenes, whether they are static or in motion. We also introduce a carefully designed dataset that serves the proposed HRI setup, denoted as (I)nteractive (V)ideo (O)bject (S)egmentation. Our IVOS dataset contains teaching videos of different objects, and manipulation tasks. Unlike previous datasets, IVOS provides manipulation tasks sequences with segmentation annotation along with the waypoints for the robot trajectories. It also provides segmentation annotation for the different transformations such as translation, scale, planar rotation, and out-of-plane rotation. Our proposed adaptation method outperforms the state-of-the-art on DAVIS and FBMS with 6.8{\%} and 1.2{\%} in F-measure respectively. It improves over the baseline on IVOS dataset with 46.1{\%} and 25.9{\%} in mIoU.},
archivePrefix = {arXiv},
arxivId = {1810.07733},
author = {Siam, Mennatullah and Jiang, Chen and Lu, Steven and Petrich, Laura and Gamal, Mahmoud and Elhoseiny, Mohamed and Jagersand, Martin},
eprint = {1810.07733},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting{\_}Siam et al.pdf:pdf},
number = {I},
title = {{Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting}},
url = {http://arxiv.org/abs/1810.07733},
year = {2018}
}
@article{Rajeswaran2018a,
abstract = {Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform a multitude of tasks in human-centric environments. However, effectively controlling them remains challenging due to their high dimensionality and large number of potential contacts. Deep reinforcement learning (DRL) provides a model-agnostic approach to control complex dynamical systems, but has not been shown to scale to high-dimensional dexterous manipulation. Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency. Consequently, the success of DRL in robotics has thus far been limited to simpler manipulators and tasks. In this work, we show that model-free DRL can effectively scale up to complex manipulation tasks with a high-dimensional 24-DoF hand, and solve them from scratch in simulated experiments. Furthermore, with the use of a small number of human demonstrations, the sample complexity can be significantly reduced, which enables learning with sample sizes equivalent to a few hours of robot experience. The use of demonstrations result in policies that exhibit very natural movements and, surprisingly, are also substantially more robust.},
archivePrefix = {arXiv},
arxivId = {arXiv:1709.10087v2},
author = {Rajeswaran, Aravind and Kumar, Vikash and Gupta, Abhishek and Vezzani, Giulia and Schulman, John and Todorov, Emanuel and Levine, Sergey},
doi = {10.15607/rss.2018.xiv.049},
eprint = {arXiv:1709.10087v2},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations{\_}Rajeswaran et al.pdf:pdf},
title = {{Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations}},
year = {2018}
}
@article{Lahoud2017a,
abstract = {In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orienta-tion for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D do-main, extensive experiments on the well-known SUN RGB-D dataset [29] show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.},
author = {Lahoud, Jean and Ghanem, Bernard},
doi = {10.1109/ICCV.2017.495},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}2D-Driven 3D Object Detection in RGB-D Images{\_}Lahoud, Ghanem.pdf:pdf},
isbn = {9781538610329},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
number = {i},
pages = {4632--4640},
title = {{2D-Driven 3D Object Detection in RGB-D Images}},
volume = {2017-Octob},
year = {2017}
}
@article{Laia,
author = {Lai, Kevin and Bo, Liefeng and Ren, Xiaofeng and Fox, Dieter},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2011{\_}Sparse Distance Learning for Object Recognition Combining RGB and Depth Information{\_}Lai et al.pdf:pdf},
number = {1},
pages = {1--25},
title = {{Hybrid Si – SiC High Power Modules}}
}
@article{Song2017,
abstract = {Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.},
archivePrefix = {arXiv},
arxivId = {arXiv:1801.06797v1},
author = {Song, Xinhang and Herranz, Luis and Jiang, Shuqiang},
eprint = {arXiv:1801.06797v1},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Depth CNNs for RGB-D scene recognition Learning from scratch better than transferring from RGB-CNNs{\_}Song, Herranz, Jiang.pdf:pdf},
journal = {31st AAAI Conference on Artificial Intelligence, AAAI 2017},
pages = {4271--4277},
title = {{Depth CNNs for RGB-D scene recognition: Learning from scratch better than transferring from RGB-CNNs}},
year = {2017}
}
@article{Wang2015c,
abstract = {Most of the feature-learning methods for RGB-D objec- t recognition either learn features from color and depth modalities separately, or simply treat RGB-D as undiffer- entiated four-channel data, which cannot adequately ex- ploit the relationship between different modalities. Moti- vated by the intuition that different modalities should con- tain not only some modal-specific patterns but also some shared common patterns, we propose a multi-modal feature learning framework for RGB-D object recognition. We first construct deep CNN layers for color and depth separately, and then connect them with our carefully designed multi- modal layers, which fuse color and depth information by enforcing a common part to be shared by features of dif- ferent modalities. In this way, we obtain features reflect- ing shared properties as well as modal-specific properties in different modalities. The information of the multi-modal learning frameworks is back-propagated to the early CNN layers. Experimental results show that our proposed multi- modal feature learning method outperforms state-of-the-art approaches on two widely used RGB-D object benchmark datasets. 1.},
author = {Wang, Anran and Cai, Jianfei and Lu, Jiwen and Cham, Tat Jen},
doi = {10.1109/ICCV.2015.134},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}MMSS Multi-modal sharable and specific feature learning for rgb-d object recognition{\_}Wang et al.pdf:pdf},
isbn = {9781467383912},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {1125--1133},
publisher = {IEEE},
title = {{MMSS: Multi-modal sharable and specific feature learning for rgb-d object recognition}},
volume = {2015 Inter},
year = {2015}
}
@article{Gaier2019a,
abstract = {Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network compared to its architecture? In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task. We propose a search method for neural network architectures that can already perform a task without any explicit weight training. To evaluate these networks, we populate the connections with a single shared weight parameter sampled from a uniform random distribution, and measure the expected performance. We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On a supervised learning domain, we find network architectures that achieve much higher than chance accuracy on MNIST using random weights. Interactive version of this paper at https://weightagnostic.github.io/},
archivePrefix = {arXiv},
arxivId = {1906.04358},
author = {Gaier, Adam and Ha, David},
eprint = {1906.04358},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Gaier, Ha - 2019 - Weight Agnostic Neural Networks.pdf:pdf},
month = {jun},
title = {{Weight Agnostic Neural Networks}},
url = {http://arxiv.org/abs/1906.04358},
year = {2019}
}
@article{Cubuk2018,
abstract = {Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5{\%} which is 0.4{\%} better than the previous record of 83.1{\%}. On CIFAR-10, we achieve an error rate of 1.5{\%}, which is 0.6{\%} better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars.},
archivePrefix = {arXiv},
arxivId = {1805.09501},
author = {Cubuk, Ekin D. and Zoph, Barret and Mane, Dandelion and Vasudevan, Vijay and Le, Quoc V.},
eprint = {1805.09501},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}AutoAugment Learning Augmentation Policies from Data{\_}Cubuk et al.pdf:pdf},
month = {may},
title = {{AutoAugment: Learning Augmentation Policies from Data}},
url = {http://arxiv.org/abs/1805.09501},
year = {2018}
}
@techreport{Kanezaki2018,
abstract = {We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10- and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves the state-of-the-art performance on an object pose estimation dataset. The code is available on https://github.com/kanezaki/rotationnet},
archivePrefix = {arXiv},
arxivId = {1603.06208v4},
author = {Kanezaki, Asako and Matsushita, Yasuyuki and Nishida, Yoshifumi},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2018.00526},
eprint = {1603.06208v4},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}RotationNet Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints{\_}Kanezaki, Matsushita, Nis.pdf:pdf},
isbn = {9781538664209},
issn = {10636919},
pages = {5010--5019},
title = {{RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints}},
url = {https://github.com/kanezaki/rotationnet},
year = {2018}
}
@techreport{Report2016,
abstract = {A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and show that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art 3D shape descriptors. Recognition rates further increase when multiple views of the shapes are provided. In addition, we present a novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering even better recognition performance. The same architecture can be applied to accurately recognize human hand-drawn sketches of shapes. We conclude that a collection of 2D views can be highly informative for 3D shape recognition and is amenable to emerging CNN architectures and their derivatives.},
archivePrefix = {arXiv},
arxivId = {1505.00880},
author = {Su, Hang and Maji, Subhransu and Kalogerakis, Evangelos and Learned-Miller, Erik},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
doi = {10.1109/ICCV.2015.114},
eprint = {1505.00880},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}Multi-view convolutional neural networks for 3D shape recognition{\_}Su et al.pdf:pdf},
isbn = {9781467383912},
issn = {15505499},
number = {02},
pages = {945--953},
pmid = {7410471},
title = {{Multi-view convolutional neural networks for 3D shape recognition}},
url = {http://vis-www.cs.umass.edu/mvcnn.},
volume = {2015 Inter},
year = {2015}
}
@article{Song2017a,
abstract = {Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.},
archivePrefix = {arXiv},
arxivId = {1801.06797},
author = {Song, Xinhang and Herranz, Luis and Jiang, Shuqiang},
eprint = {1801.06797},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Depth CNNs for RGB-D scene recognition Learning from scratch better than transferring from RGB-CNNs{\_}Song, Herranz, Jiang.pdf:pdf},
journal = {31st AAAI Conference on Artificial Intelligence, AAAI 2017},
month = {jan},
pages = {4271--4277},
title = {{Depth CNNs for RGB-D scene recognition: Learning from scratch better than transferring from RGB-CNNs}},
url = {http://arxiv.org/abs/1801.06797},
year = {2017}
}
@article{Rodol,
author = {Rodol, Emanuele and Harada, Tatsuya and Kanezaki, Asako and A, Emanuele Rodol},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Rodol et al. - Unknown - グラフマッチング学習を用いたrgb-D画像からの物体検出.pdf:pdf},
keywords = {3d shape,gradient descent method,graph matching,keypoint matching,optimization},
number = {2},
title = {{グラフマッチング学習を用いたrgb-D画像からの物体検出}},
volume = {1}
}
@article{Wang2015b,
abstract = {Most of the feature-learning methods for RGB-D objec- t recognition either learn features from color and depth modalities separately, or simply treat RGB-D as undiffer- entiated four-channel data, which cannot adequately ex- ploit the relationship between different modalities. Moti- vated by the intuition that different modalities should con- tain not only some modal-specific patterns but also some shared common patterns, we propose a multi-modal feature learning framework for RGB-D object recognition. We first construct deep CNN layers for color and depth separately, and then connect them with our carefully designed multi- modal layers, which fuse color and depth information by enforcing a common part to be shared by features of dif- ferent modalities. In this way, we obtain features reflect- ing shared properties as well as modal-specific properties in different modalities. The information of the multi-modal learning frameworks is back-propagated to the early CNN layers. Experimental results show that our proposed multi- modal feature learning method outperforms state-of-the-art approaches on two widely used RGB-D object benchmark datasets. 1.},
author = {Wang, Anran and Cai, Jianfei and Lu, Jiwen and Cham, Tat Jen},
doi = {10.1109/ICCV.2015.134},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}MMSS Multi-modal sharable and specific feature learning for rgb-d object recognition{\_}Wang et al.pdf:pdf},
isbn = {9781467383912},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {1125--1133},
publisher = {IEEE},
title = {{MMSS: Multi-modal sharable and specific feature learning for rgb-d object recognition}},
volume = {2015 Inter},
year = {2015}
}
@article{Lai,
abstract = {In this work we address joint object category and instance recognition in the context of RGB-D (depth) cameras. Motivated by local distance learning, where a novel view of an object is compared to individual views of previously seen objects, we define a view-to-object distance where a novel view is compared simultaneously to all views of a previous object. This novel distance is based on a weighted combination of feature differences between views. We show, through jointly learning per-view weights, that this measure leads to superior classification performance on object category and instance recognition. More importantly, the proposed distance allows us to find a sparse solution via Group-Lasso regularization, where a small subset of representative views of an object is identified and used, with the rest discarded. This significantly reduces computational cost without compromising recognition accuracy. We evaluate the proposed technique, Instance Distance Learning (IDL), on the RGB-D Object Dataset, which consists of 300 object instances in 51 everyday categories and about 250,000 views of objects with both RGB color and depth. We empirically compare IDL to several alternative state-of-the-art approaches and also validate the use of visual and shape cues and their combination.},
author = {Lai, Kevin and Bo, Liefeng and Ren, Xiaofeng and Fox, Dieter},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2011{\_}Sparse Distance Learning for Object Recognition Combining RGB and Depth Information{\_}Lai et al.pdf:pdf},
journal = {IEEE International Conference on Robotucs and Automation},
number = {1},
pages = {1--25},
title = {{Sparse Distance Learning for Object Recognition Combining RGB and Depth Information}},
url = {https://rse-lab.cs.washington.edu/postscripts/sparse-distance-icra-11.pdf},
year = {2011}
}
@techreport{Lahoud2017,
abstract = {In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orienta-tion for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D do-main, extensive experiments on the well-known SUN RGB-D dataset [29] show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.},
author = {Lahoud, Jean and Ghanem, Bernard},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
doi = {10.1109/ICCV.2017.495},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}2D-Driven 3D Object Detection in RGB-D Images{\_}Lahoud, Ghanem.pdf:pdf},
isbn = {9781538610329},
issn = {15505499},
pages = {4632--4640},
title = {{2D-Driven 3D Object Detection in RGB-D Images}},
url = {https://ivul.kaust.edu.sa/Documents/Publications/2017/2D-Driven 3D Object Detection in RGB-D Images.pdf},
volume = {2017-Octob},
year = {2017}
}
@article{Rajeswaran2018,
abstract = {Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform a multitude of tasks in human-centric environments. However, effectively controlling them remains challenging due to their high dimensionality and large number of potential contacts. Deep reinforcement learning (DRL) provides a model-agnostic approach to control complex dynamical systems, but has not been shown to scale to high-dimensional dexterous manipulation. Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency. Consequently, the success of DRL in robotics has thus far been limited to simpler manipulators and tasks. In this work, we show that model-free DRL can effectively scale up to complex manipulation tasks with a high-dimensional 24-DoF hand, and solve them from scratch in simulated experiments. Furthermore, with the use of a small number of human demonstrations, the sample complexity can be significantly reduced, which enables learning with sample sizes equivalent to a few hours of robot experience. The use of demonstrations result in policies that exhibit very natural movements and, surprisingly, are also substantially more robust.},
archivePrefix = {arXiv},
arxivId = {1709.10087},
author = {Rajeswaran, Aravind and Kumar, Vikash and Gupta, Abhishek and Vezzani, Giulia and Schulman, John and Todorov, Emanuel and Levine, Sergey},
doi = {10.15607/rss.2018.xiv.049},
eprint = {1709.10087},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations{\_}Rajeswaran et al.pdf:pdf},
month = {sep},
title = {{Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations}},
url = {http://arxiv.org/abs/1709.10087},
year = {2018}
}
@article{Chiu2019,
abstract = {Predicting the future is an important aspect for decision-making in robotics or autonomous driving systems, which heavily rely upon visual scene understanding. While prior work attempts to predict future video pixels, anticipate activities or forecast future scene semantic segments from segmentation of the preceding frames, methods that predict future semantic segmentation solely from the previous frame RGB data in a single end-to-end trainable model do not exist. In this paper, we propose a temporal encoder-decoder network architecture that encodes RGB frames from the past and decodes the future semantic segmentation. The network is coupled with a new knowledge distillation training framework specifically for the forecasting task. Our method, only seeing preceding video frames, implicitly models the scene segments while simultaneously accounting for the object dynamics to infer the future scene semantic segments. Our results on Cityscapes outperform the baseline and current state-of-the-art methods. Code is available at https://github.com/eddyhkchiu/segmenting{\_}the{\_}future/.},
archivePrefix = {arXiv},
arxivId = {1904.10666},
author = {Chiu, Hsu-kuang and Adeli, Ehsan and Niebles, Juan Carlos},
eprint = {1904.10666},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Segmenting the Future{\_}Chiu, Adeli, Niebles.pdf:pdf},
month = {apr},
title = {{Segmenting the Future}},
url = {http://arxiv.org/abs/1904.10666},
year = {2019}
}
@article{Siam2018,
abstract = {Video object segmentation is an essential task in robot manipulation to facilitate grasping and learning affordances. Incremental learning is important for robotics in unstructured environments, since the total number of objects and their variations can be intractable. Inspired by the children learning process, human robot interaction (HRI) can be utilized to teach robots about the world guided by humans similar to how children learn from a parent or a teacher. A human teacher can show potential objects of interest to the robot, which is able to self adapt to the teaching signal without providing manual segmentation labels. We propose a novel teacher-student learning paradigm to teach robots about their surrounding environment. A two-stream motion and appearance "teacher" network provides pseudo-labels to adapt an appearance "student" network. The student network is able to segment the newly learned objects in other scenes, whether they are static or in motion. We also introduce a carefully designed dataset that serves the proposed HRI setup, denoted as (I)nteractive (V)ideo (O)bject (S)egmentation. Our IVOS dataset contains teaching videos of different objects, and manipulation tasks. Unlike previous datasets, IVOS provides manipulation tasks sequences with segmentation annotation along with the waypoints for the robot trajectories. It also provides segmentation annotation for the different transformations such as translation, scale, planar rotation, and out-of-plane rotation. Our proposed adaptation method outperforms the state-of-the-art on DAVIS and FBMS with 6.8{\%} and 1.2{\%} in F-measure respectively. It improves over the baseline on IVOS dataset with 46.1{\%} and 25.9{\%} in mIoU.},
archivePrefix = {arXiv},
arxivId = {1810.07733},
author = {Siam, Mennatullah and Jiang, Chen and Lu, Steven and Petrich, Laura and Gamal, Mahmoud and Elhoseiny, Mohamed and Jagersand, Martin},
eprint = {1810.07733},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting{\_}Siam et al.pdf:pdf},
month = {oct},
title = {{Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting}},
url = {http://arxiv.org/abs/1810.07733},
year = {2018}
}
@article{Vecerik2018a,
abstract = {Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumbersome to implement, and must be tailored to each individual problem by a qualified engineer. Within the learning community there is a long history of insertion research, but existing approaches are typically either too sample-inefficient to run on real robots, or assume access to high-level object features, e.g. socket pose. In this paper we show that relatively minor modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a small number of human demonstrations, allows the robot to quickly learn to solve these tasks efficiently and robustly. Our approach requires no modeling or simulation, no parameterized search or alignment behaviors, no vision system aside from raw images, and no reward shaping. We evaluate our approach on a narrow-clearance peg-insertion task and a deformable clip-insertion task, both of which include variability in the socket position. Our results show that these tasks can be solved reliably on the real robot in less than 10 minutes of interaction time, and that the resulting policies are robust to variance in the socket position and orientation.},
archivePrefix = {arXiv},
arxivId = {1810.01531},
author = {Vecerik, Mel and Sushkov, Oleg and Barker, David and Roth{\"{o}}rl, Thomas and Hester, Todd and Scholz, Jon},
eprint = {1810.01531},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning{\_}Vecerik et al.pdf:pdf},
month = {oct},
title = {{A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1810.01531},
year = {2018}
}
@article{Touvron2019,
abstract = {Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between the typical size of the objects seen by the classifier at train and test time. We experimentally validate that, for a target test resolution, using a lower train resolution offers better classification at test time. We then propose a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ. It involves only a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images. For instance, we obtain 77.1{\%} top-1 accuracy on ImageNet with a ResNet-50 trained on 128x128 images, and 79.8{\%} with one trained on 224x224 image. In addition, if we use extra training data we get 82.5{\%} with the ResNet-50 train with 224x224 images. Conversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86.4{\%} (top-5: 98.0{\%}) (single-crop). To the best of our knowledge this is the highest ImageNet single-crop, top-1 and top-5 accuracy to date.},
archivePrefix = {arXiv},
arxivId = {1906.06423},
author = {Touvron, Hugo and Vedaldi, Andrea and Douze, Matthijs and J{\'{e}}gou, Herv{\'{e}}},
eprint = {1906.06423},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Fixing the train-test resolution discrepancy{\_}Touvron et al.pdf:pdf},
month = {jun},
title = {{Fixing the train-test resolution discrepancy}},
url = {http://arxiv.org/abs/1906.06423},
year = {2019}
}
@inproceedings{VanHoof2016,
abstract = {{\textcopyright} 2016 IEEE. For many tasks, tactile or visual feedback is helpful or even crucial. However, designing controllers that take such high-dimensional feedback into account is non-trivial. Therefore, robots should be able to learn tactile skills through trial and error by using reinforcement learning algorithms. The input domain for such tasks, however, might include strongly correlated or non-relevant dimensions, making it hard to specify a suitable metric on such domains. Auto-encoders specialize in finding compact representations, where defining such a metric is likely to be easier. Therefore, we propose a reinforcement learning algorithm that can learn non-linear policies in continuous state spaces, which leverages representations learned using auto-encoders. We first evaluate this method on a simulated toytask with visual input. Then, we validate our approach on a real-robot tactile stabilization task.},
author = {{Van Hoof}, Herke and Chen, Nutan and Karl, Maximilian and {Van Der Smagt}, Patrick and Peters, Jan},
booktitle = {IEEE International Conference on Intelligent Robots and Systems},
doi = {10.1109/IROS.2016.7759578},
isbn = {9781509037629},
issn = {21530866},
pages = {3928--3934},
title = {{Stable reinforcement learning with autoencoders for tactile and visual data}},
volume = {2016-Novem},
year = {2016}
}
@article{George2019,
abstract = {We describe use of a bidirectional neuromyoelectric prosthetic hand that conveys biomimetic sensory feedback. Electromyographic recordings from residual arm muscles were decoded to provide independent and proportional control of a six-DOF prosthetic hand and wrist—the DEKA LUKE arm. Activation of contact sensors on the prosthesis resulted in intraneural microstimulation of residual sensory nerve fibers through chronically implanted Utah Slanted Electrode Arrays, thereby evoking tactile percepts on the phantom hand. With sensory feedback enabled, the participant exhibited greater precision in grip force and was better able to handle fragile objects. With active exploration, the participant was also able to distinguish between small and large objects and between soft and hard ones. When the sensory feedback was biomimetic—designed to mimic natural sensory signals—the participant was able to identify the objects significantly faster than with the use of traditional encoding algorithms that depended on only the present stimulus intensity. Thus, artificial touch can be sculpted by patterning the sensory feedback, and biologically inspired patterns elicit more interpretable and useful percepts.},
author = {George, J. A. and Kluger, D. T. and Davis, T. S. and Wendelken, S. M. and Okorokova, E. V. and He, Q. and Duncan, C. C. and Hutchinson, D. T. and Thumser, Z. C. and Beckler, D. T. and Marasco, P. D. and Bensmaia, S. J. and Clark, G. A.},
doi = {10.1126/scirobotics.aax2352},
issn = {2470-9476},
journal = {Science Robotics},
month = {jul},
number = {32},
pages = {eaax2352},
publisher = {Science Robotics},
title = {{Biomimetic sensory feedback through peripheral nerve stimulation improves dexterous use of a bionic hand}},
url = {http://robotics.sciencemag.org/lookup/doi/10.1126/scirobotics.aax2352},
volume = {4},
year = {2019}
}
@article{Kim2019,
abstract = {We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier. Unlike previous attention-based methods which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes. Moreover, our new AdaLIN (Adaptive Layer-Instance Normalization) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending on datasets. Experimental results show the superiority of the proposed method compared to the existing state-of-the-art models with a fixed network architecture and hyper-parameters.},
archivePrefix = {arXiv},
arxivId = {1907.10830},
author = {Kim, Junho and Kim, Minjae and Kang, Hyeonwoo and Lee, Kwanghee},
eprint = {1907.10830},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Kim et al. - 2019 - U-GAT-IT Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image.pdf:pdf},
month = {jul},
title = {{U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation}},
url = {http://arxiv.org/abs/1907.10830},
year = {2019}
}
@article{Xue2019,
abstract = {Cervical intraepithelial neoplasia (CIN) grade of histopathology images is a crucial indicator in cervical biopsy results. Accurate CIN grading of epithelium regions helps pathologists with precancerous lesion diagnosis and treatment planning. Although an automated CIN grading system has been desired, supervised training of such a system would require a large amount of expert annotations, which are expensive and time-consuming to collect. In this paper, we investigate the CIN grade classification problem on segmented epithelium patches. We propose to use conditional Generative Adversarial Networks (cGANs) to expand the limited training dataset, by synthesizing realistic cervical histopathology images. While the synthetic images are visually appealing, they are not guaranteed to contain meaningful features for data augmentation. To tackle this issue, we propose a synthetic-image filtering mechanism based on the divergence in feature space between generated images and class centroids in order to control the feature quality of selected synthetic images for data augmentation. Our models are evaluated on a cervical histopathology image dataset with a limited number of patch-level CIN grade annotations. Extensive experimental results show a significant improvement of classification accuracy from 66.3{\%} to 71.7{\%} using the same ResNet18 baseline classifier after leveraging our cGAN generated images with feature-based filtering, which demonstrates the effectiveness of our models.},
archivePrefix = {arXiv},
arxivId = {1907.10655},
author = {Xue, Yuan and Zhou, Qianying and Ye, Jiarong and Long, L. Rodney and Antani, Sameer and Cornwell, Carl and Xue, Zhiyun and Huang, Xiaolei},
eprint = {1907.10655},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification{\_}Xue et al.pdf:pdf},
month = {jul},
title = {{Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification}},
url = {http://arxiv.org/abs/1907.10655},
year = {2019}
}
@article{Babin2019,
abstract = {The stable and repeatable grasping of objects lying on a flat hard surface is addressed in this paper. A physical model of an object lying on a flat surface and its interaction with the environment and with a gripper is proposed. The important parameters governing the interaction are obtained. From this model, a grasping procedure is established and a robotic gripper is modified in order to grant the ability to pick up large thin objects lying on smooth hard surfaces. The procedure is implemented to demonstrate its repeatability on a chosen set of objects. It is shown that by sensing the force applied on the object and by taking advantage of the nature of the contact provided by the passive joint of the modified finger, a wide range of previously not directly graspable objects are made graspable via the application of a general approach. The experimental results reported clearly show the benefits of the simple force sensing implemented in the gripper as well as of the use of passive joints when interacting with very stiff environments. The proposed approach, while simple, yields a repeatable solution to a complex manipulation problem.},
author = {Babin, Vincent and St-Onge, David and Gosselin, Cl{\'{e}}ment},
doi = {10.1016/j.rcim.2018.06.002},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Stable and repeatable grasping of flat objects on hard surfaces using passive and epicyclic mechanisms{\_}Babin, St-Onge, Gosselin.pdf:pdf},
issn = {07365845},
journal = {Robotics and Computer-Integrated Manufacturing},
keywords = {Constrained grasping,Epicyclic mechanism,Gripper,Passive finger,Repeated grasping,Robot grasping},
month = {feb},
pages = {1--10},
publisher = {Pergamon},
title = {{Stable and repeatable grasping of flat objects on hard surfaces using passive and epicyclic mechanisms}},
url = {https://www.sciencedirect.com/science/article/pii/S0736584518301297},
volume = {55},
year = {2019}
}
@article{Agostinelli2019,
abstract = {The Rubik's cube is a prototypical combinatorial puzzle that has a large state space with a single goal state. The goal state is unlikely to be accessed using sequences of randomly generated moves, posing unique challenges for machine learning. We solve the Rubik's cube with DeepCubeA, a deep reinforcement learning approach that learns how to solve increasingly difficult states in reverse from the goal state without any specific domain knowledge. DeepCubeA solves 100{\%} of all test configurations, finding a shortest path to the goal state 60.3{\%} of the time. DeepCubeA generalizes to other combinatorial puzzles and is able to solve the 15 puzzle, 24 puzzle, 35 puzzle, 48 puzzle, Lights Out and Sokoban, finding a shortest path in the majority of verifiable cases.},
author = {Agostinelli, Forest and McAleer, Stephen and Shmakov, Alexander and Baldi, Pierre},
doi = {10.1038/s42256-019-0070-z},
issn = {2522-5839},
journal = {Nature Machine Intelligence},
keywords = {Computational science,Computer science},
month = {jul},
pages = {1},
publisher = {Nature Publishing Group},
title = {{Solving the Rubik's cube with deep reinforcement learning and search}},
url = {http://www.nature.com/articles/s42256-019-0070-z},
year = {2019}
}
@article{Billard2019,
abstract = {{\textless}p{\textgreater}Dexterous manipulation is one of the primary goals in robotics. Robots with this capability could sort and package objects, chop vegetables, and fold clothes. As robots come to work side by side with humans, they must also become human-aware. Over the past decade, research has made strides toward these goals. Progress has come from advances in visual and haptic perception and in mechanics in the form of soft actuators that offer a natural compliance. Most notably, immense progress in machine learning has been leveraged to encapsulate models of uncertainty and to support improvements in adaptive and robust control. Open questions remain in terms of how to enable robots to deal with the most unpredictable agent of all, the human.{\textless}/p{\textgreater}},
author = {Billard, Aude and Kragic, Danica},
doi = {10.1126/science.aat8414},
file = {:Users/atsushi/Downloads/eaat8414.full.pdf:pdf},
issn = {0036-8075},
journal = {Science},
number = {6446},
pages = {eaat8414},
title = {{Trends and challenges in robot manipulation}},
url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aat8414},
volume = {364},
year = {2019}
}
@article{Brown2019,
abstract = {{\textless}p{\textgreater}In recent years there have been great strides in artificial intelligence (AI), with games often serving as challenge problems, benchmarks, and milestones for progress. Poker has served for decades as such a challenge problem. Past successes in such benchmarks, including poker, have been limited to two-player games. However, poker in particular is traditionally played with more than two players. Multiplayer games present fundamental additional issues beyond those in two-player games, and multiplayer poker is a recognized AI milestone. In this paper we present Pluribus, an AI that we show is stronger than top human professionals in six-player no-limit Texas hold'em poker, the most popular form of poker played by humans.{\textless}/p{\textgreater}},
author = {Brown, Noam and Sandholm, Tuomas},
doi = {10.1126/science.aay2400},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Brown, Sandholm - 2019 - Superhuman AI for multiplayer poker.pdf:pdf},
issn = {0036-8075},
journal = {Science},
month = {jul},
pages = {eaay2400},
publisher = {American Association for the Advancement of Science},
title = {{Superhuman AI for multiplayer poker}},
url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aay2400},
year = {2019}
}
@article{Sharma2019,
abstract = {We introduce a computationally-efficient CNN micro-architecture Slim Module to design a lightweight deep neural network Slim-Net for face attribute prediction. Slim Modules are constructed by assembling depthwise separable convolutions with pointwise convolution to produce a computationally efficient module. The problem of facial attribute prediction is challenging because of the large variations in pose, background, illumination, and dataset imbalance. We stack these Slim Modules to devise a compact CNN which still maintains very high accuracy. Additionally, the neural network has a very low memory footprint which makes it suitable for mobile and embedded applications. Experiments on the CelebA dataset show that Slim-Net achieves an accuracy of 91.24{\%} with at least 25 times fewer parameters than comparably performing methods, which reduces the memory storage requirement of Slim-net by at least 87{\%}.},
archivePrefix = {arXiv},
arxivId = {1907.02157},
author = {Sharma, Ankit and Foroosh, Hassan},
eprint = {1907.02157},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Sharma, Foroosh - 2019 - Slim-CNN A Light-Weight CNN for Face Attribute Prediction.pdf:pdf},
month = {jul},
title = {{Slim-CNN: A Light-Weight CNN for Face Attribute Prediction}},
url = {http://arxiv.org/abs/1907.02157},
year = {2019}
}
@article{Qin2019,
abstract = {In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information. Different from previous methods using pixel-level depth maps, we propose employing 3D anchors to explicitly construct object-level correspondences between the regions of interest in stereo images, from which the deep neural network learns to detect and triangulate the targeted object in 3D space. We also introduce a cost-efficient channel reweighting strategy that enhances representational features and weakens noisy signals to facilitate the learning process. All of these are flexibly integrated into a solid baseline detector that uses monocular images. We demonstrate that both the monocular baseline and the stereo triangulation learning network outperform the prior state-of-the-arts in 3D object detection and localization on the challenging KITTI dataset.},
archivePrefix = {arXiv},
arxivId = {1906.01193},
author = {Qin, Zengyi and Wang, Jinglu and Lu, Yan},
eprint = {1906.01193},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Qin, Wang, Lu - 2019 - Triangulation Learning Network from Monocular to Stereo 3D Object Detection.pdf:pdf},
month = {jun},
title = {{Triangulation Learning Network: from Monocular to Stereo 3D Object Detection}},
url = {http://arxiv.org/abs/1906.01193},
year = {2019}
}
@techreport{Razavi2019,
abstract = {We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.},
archivePrefix = {arXiv},
arxivId = {1906.00446},
author = {Razavi, Ali and van den Oord, Aaron and Vinyals, Oriol},
eprint = {1906.00446},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Razavi, van den Oord, Vinyals DeepMind - Unknown - Generating Diverse High-Fidelity Images with VQ-VAE-2.pdf:pdf},
title = {{Generating Diverse High-Fidelity Images with VQ-VAE-2}},
url = {http://arxiv.org/abs/1906.00446},
year = {2019}
}
@techreport{Luketina2011,
abstract = {To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand. Recent advances in representation learning for language make it possible to build models that acquire world knowledge from text corpora and integrate this knowledge into downstream decision making problems. We thus argue that the time is right to investigate a tight integration of natural language understanding into RL in particular. We survey the state of the field, including work on instruction following, text games, and learning from textual domain knowledge. Finally , we call for the development of new environments as well as further investigation into the potential uses of recent Natural Language Processing (NLP) techniques for such tasks.},
author = {Luketina, Jelena and Nardelli, Nantas and Farquhar, Gregory and Foerster, Jakob and Andreas, Jacob and Grefenstette, Edward and Whiteson, Shimon and Rockt, Tim},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Luketina et al. - Unknown - A Survey of Reinforcement Learning Informed by Natural Language.pdf:pdf},
title = {{A Survey of Reinforcement Learning Informed by Natural Language}},
url = {http://www.cs.ox.ac.uk/people/shimon.whiteson/pubs/luketinaijcai19.pdf},
year = {2011}
}
@article{Lee2018c,
abstract = {Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. However, it is non-trivial to manually design a robot controller that combines modalities with very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. We use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. We evaluate our method on a peg insertion task, generalizing over different geometry, configurations, and clearances, while being robust to external perturbations. Results for simulated and real robot experiments are presented.},
archivePrefix = {arXiv},
arxivId = {1810.10191},
author = {Lee, Michelle A. and Zhu, Yuke and Srinivasan, Krishnan and Shah, Parth and Savarese, Silvio and Fei-Fei, Li and Garg, Animesh and Bohg, Jeannette},
eprint = {1810.10191},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Making Sense of Vision and Touch Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks{\_}Lee et al.pdf:pdf},
title = {{Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks}},
url = {http://arxiv.org/abs/1810.10191},
year = {2018}
}
@article{Takahashi2018,
abstract = {Estimation of tactile properties from vision, such as slipperiness or roughness, is important to effectively interact with the environment. These tactile properties help us decide which actions we should choose and how to perform them. E.g., we can drive slower if we see that we have bad traction or grasp tighter if an item looks slippery. We believe that this ability also helps robots to enhance their understanding of the environment, and thus enables them to tailor their actions to the situation at hand. We therefore propose a model to estimate the degree of tactile properties from visual perception alone (e.g., the level of slipperiness or roughness). Our method extends a encoder-decoder network, in which the latent variables are visual and tactile features. In contrast to previous works, our method does not require manual labeling, but only RGB images and the corresponding tactile sensor data. All our data is collected with a webcam and uSkin tactile sensor mounted on the end-effector of a Sawyer robot, which strokes the surfaces of 25 different materials. We show that our model generalizes to materials not included in the training data by evaluating the feature space, indicating that it has learned to associate important tactile properties with images.},
archivePrefix = {arXiv},
arxivId = {1803.03435},
author = {Takahashi, Kuniyuki and Tan, Jethro},
eprint = {1803.03435},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Deep Visuo-Tactile Learning Estimation of Tactile Properties from Images{\_}Takahashi, Tan.pdf:pdf},
pages = {1--7},
title = {{Deep Visuo-Tactile Learning: Estimation of Tactile Properties from Images}},
url = {http://arxiv.org/abs/1803.03435},
year = {2018}
}
@article{Araki2018,
abstract = {Accurate grasping of objects such as industrial parts and everyday necessities is an important task for industrial robots and living-support robots. Many methods have been proposed for grasp point detection for robots, some that utilize machine learning and some that do not. Recently, a grasp point detection method using a 2-stage deep neural network has been proposed. Although the 2-stage deep neural network could detect the grasping point of no-learned objects, the computation cost would be high. In this paper, we propose a method for detecting grasping points using one deep convolutional neural network (DCNN) introducing graspability. Simultaneous detection of grasping points and graspability in one neural network lessens calculation costs. Evaluation experiments confirmed that grasping points could be properly detected using graspability.},
author = {Araki, Ryosuke and Hasegawa, Takahiro and Yamauchi, Yuji and Yamashita, Takayoshi and Fujiyoshi, Hironobu and Domae, Yukiyasu and Kawanishi, Ryosuke and Seki, Makito},
doi = {10.7210/jrsj.36.559},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Araki et al. - 2018 - Graspabilityを導入したDCNNによる物体把持位置検出.pdf:pdf},
issn = {0289-1824},
journal = {Journal of the Robotics Society of Japan},
month = {nov},
number = {8},
pages = {559--566},
publisher = {The Robotics Society of Japan},
title = {{Graspabilityを導入したDCNNによる物体把持位置検出}},
volume = {36},
year = {2018}
}
@article{Garcia-Garcia2017,
abstract = {Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.},
archivePrefix = {arXiv},
arxivId = {1704.06857},
author = {Garcia-Garcia, Alberto and Orts-Escolano, Sergio and Oprea, Sergiu and Villena-Martinez, Victor and Garcia-Rodriguez, Jose},
eprint = {1704.06857},
pages = {1--23},
title = {{A Review on Deep Learning Techniques Applied to Semantic Segmentation}},
url = {http://arxiv.org/abs/1704.06857},
year = {2017}
}
@article{Hatori2018,
abstract = {Comprehension of spoken natural language is an essential component for robots to communicate with human effectively. However, handling unconstrained spoken instructions is challenging due to (1) complex structures including a wide variety of expressions used in spoken language and (2) inherent ambiguity in interpretation of human instructions. In this paper, we propose the first comprehensive system that can handle unconstrained spoken language and is able to effectively resolve ambiguity in spoken instructions. Specifically, we integrate deep-learning-based object detection together with natural language processing technologies to handle unconstrained spoken instructions, and propose a method for robots to resolve instruction ambiguity through dialogue. Through our experiments on both a simulated environment as well as a physical industrial robot arm, we demonstrate the ability of our system to understand natural instructions from human operators effectively, and how higher success rates of the object picking task can be achieved through an interactive clarification process.},
author = {Hatori, Jun and Kikuchi, Yuta and Kobayashi, Sosuke and Takahashi, Kuniyuki and Tsuboi, Yuta and Unno, Yuya and Ko, Wilson and Tan, Jethro},
doi = {10.1109/ICRA.2018.8460699},
isbn = {9781538630815},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
pages = {3774--3781},
publisher = {IEEE},
title = {{Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions}},
year = {2018}
}
@article{Sevastopolsky2018,
abstract = {In this work, we propose a special cascade network for image segmentation, which is based on the U-Net networks as building blocks and the idea of the iterative refinement. The model was mainly applied to achieve higher recognition quality for the task of finding borders of the optic disc and cup, which are relevant to the presence of glaucoma. Compared to a single U-Net and the state-of-the-art methods for the investigated tasks, very high segmentation quality has been achieved without a need for increasing the volume of datasets. Our experiments include comparison with the best-known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS, and evaluation on a private data set collected in collaboration with University of California San Francisco Medical School. The analysis of the architecture details is presented, and it is argued that the model can be employed for a broad scope of image segmentation problems of similar nature.},
archivePrefix = {arXiv},
arxivId = {1804.11294},
author = {Sevastopolsky, Artem and Drapak, Stepan and Kiselev, Konstantin and Snyder, Blake M. and Keenan, Jeremy D. and Georgievskaya, Anastasia},
eprint = {1804.11294},
title = {{Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup}},
url = {http://arxiv.org/abs/1804.11294},
year = {2018}
}
@article{Zou2019,
abstract = {Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.},
archivePrefix = {arXiv},
arxivId = {1905.05055},
author = {Zou, Zhengxia and Shi, Zhenwei and Guo, Yuhong and Ye, Jieping},
eprint = {1905.05055},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Zou et al. - 2019 - Object Detection in 20 Years A Survey.pdf:pdf},
month = {may},
title = {{Object Detection in 20 Years: A Survey}},
url = {http://arxiv.org/abs/1905.05055},
year = {2019}
}
@article{Liu2018b,
abstract = {Generic object detection, aiming at locating object instances from a large number of predefined categories in natural images, is one of the most fundamental and challenging problems in computer vision. Deep learning techniques have emerged in recent years as powerful methods for learning feature representations directly from data, and have led to remarkable breakthroughs in the field of generic object detection. Given this time of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought by deep learning techniques. More than 250 key contributions are included in this survey, covering many aspects of generic object detection research: leading detection frameworks and fundamental subproblems including object feature representation, object proposal generation, context information modeling and training strategies; evaluation issues, specifically benchmark datasets, evaluation metrics, and state of the art performance. We finish by identifying promising directions for future research.},
archivePrefix = {arXiv},
arxivId = {1809.02165},
author = {Liu, Li and Ouyang, Wanli and Wang, Xiaogang and Fieguth, Paul and Liu, Xinwang and Pietik{\"{a}}inen, Matti},
eprint = {1809.02165},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Liu et al. - 2018 - Deep Learning for Generic Object Detection A Survey.pdf:pdf},
month = {sep},
title = {{Deep Learning for Generic Object Detection: A Survey}},
url = {http://arxiv.org/abs/1809.02165},
year = {2018}
}
@article{Wang2019,
abstract = {Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks. However, little effort has been devoted to establishing convolution in non-linear space. Existing works mainly leverage on the activation layers, which can only provide point-wise non-linearity. To solve this problem, a new operation, kervolution (kernel convolution), is introduced to approximate complex behaviors of human perception systems leveraging on the kernel trick. It generalizes convolution, enhances the model capacity, and captures higher order interactions of features, via patch-wise kernel functions, but without introducing additional parameters. Extensive experiments show that kervolutional neural networks (KNN) achieve higher accuracy and faster convergence than baseline CNN.},
archivePrefix = {arXiv},
arxivId = {1904.03955},
author = {Wang, Chen and Yang, Jianfei and Xie, Lihua and Yuan, Junsong},
eprint = {1904.03955},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Wang et al. - 2019 - Kervolutional Neural Networks.pdf:pdf},
month = {apr},
title = {{Kervolutional Neural Networks}},
url = {http://arxiv.org/abs/1904.03955},
year = {2019}
}
@article{Chebotar2018,
abstract = {We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the randomization of simulations, we adapt the simulation parameter distribution using a few real world roll-outs interleaved with policy training. In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world. We show that policies trained with our method are able to reliably transfer to different robots in two real world tasks: swing-peg-in-hole and opening a cabinet drawer. The video of our experiments can be found at https://sites.google.com/view/simopt},
archivePrefix = {arXiv},
arxivId = {1810.05687},
author = {Chebotar, Yevgen and Handa, Ankur and Makoviychuk, Viktor and Macklin, Miles and Issac, Jan and Ratliff, Nathan and Fox, Dieter},
eprint = {1810.05687},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Chebotar et al. - 2018 - Closing the Sim-to-Real Loop Adapting Simulation Randomization with Real World Experience.pdf:pdf},
month = {oct},
title = {{Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience}},
url = {http://arxiv.org/abs/1810.05687},
year = {2018}
}
@article{Amini2018,
abstract = {Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. While there have been recent advances on extensions to handle forms of navigation instruction, these works are unable to capture the full distribution of possible actions that could be taken and to reason about localization of the robot within the environment. In this paper, we extend end-to-end driving networks with the ability to understand maps. We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map. Additionally, we formulate how our model can be used to localize the robot according to correspondences between the map and the observed visual road topology, inspired by the rough localization that human drivers can perform. We evaluate our algorithms on real-world driving data, and reason about the robustness of the inferred steering commands under various types of rich driving scenarios. In addition, we evaluate our localization algorithm over a new set of roads and intersections which the model has never driven through and demonstrate rough localization in situations without any GPS prior.},
archivePrefix = {arXiv},
arxivId = {1811.10119},
author = {Amini, Alexander and Rosman, Guy and Karaman, Sertac and Rus, Daniela},
eprint = {1811.10119},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Amini et al. - 2018 - Variational End-to-End Navigation and Localization.pdf:pdf},
month = {nov},
title = {{Variational End-to-End Navigation and Localization}},
url = {http://arxiv.org/abs/1811.10119},
year = {2018}
}
@article{Takahashi2018,
abstract = {Estimation of tactile properties from vision, such as slipperiness or roughness, is important to effectively interact with the environment. These tactile properties help us decide which actions we should choose and how to perform them. E.g., we can drive slower if we see that we have bad traction or grasp tighter if an item looks slippery. We believe that this ability also helps robots to enhance their understanding of the environment, and thus enables them to tailor their actions to the situation at hand. We therefore propose a model to estimate the degree of tactile properties from visual perception alone (e.g., the level of slipperiness or roughness). Our method extends a encoder-decoder network, in which the latent variables are visual and tactile features. In contrast to previous works, our method does not require manual labeling, but only RGB images and the corresponding tactile sensor data. All our data is collected with a webcam and uSkin tactile sensor mounted on the end-effector of a Sawyer robot, which strokes the surfaces of 25 different materials. We show that our model generalizes to materials not included in the training data by evaluating the feature space, indicating that it has learned to associate important tactile properties with images.},
archivePrefix = {arXiv},
arxivId = {1803.03435},
author = {Takahashi, Kuniyuki and Tan, Jethro},
eprint = {1803.03435},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Takahashi, Tan - 2018 - Deep Visuo-Tactile Learning Estimation of Tactile Properties from Images.pdf:pdf},
month = {mar},
title = {{Deep Visuo-Tactile Learning: Estimation of Tactile Properties from Images}},
url = {http://arxiv.org/abs/1803.03435},
year = {2018}
}
@article{Lee2018b,
abstract = {Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. However, it is non-trivial to manually design a robot controller that combines modalities with very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. We use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. We evaluate our method on a peg insertion task, generalizing over different geometry, configurations, and clearances, while being robust to external perturbations. Results for simulated and real robot experiments are presented.},
archivePrefix = {arXiv},
arxivId = {1810.10191},
author = {Lee, Michelle A. and Zhu, Yuke and Srinivasan, Krishnan and Shah, Parth and Savarese, Silvio and Fei-Fei, Li and Garg, Animesh and Bohg, Jeannette},
eprint = {1810.10191},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Lee et al. - 2018 - Making Sense of Vision and Touch Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks.pdf:pdf},
month = {oct},
title = {{Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks}},
url = {http://arxiv.org/abs/1810.10191},
year = {2018}
}
@article{Watkins2005,
abstract = {Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989). We show thatQ-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where manyQ values can be changed each iteration, rather than just one.},
author = {Watkins, Christopher J. C. H. and Dayan, Peter},
doi = {10.1007/bf00992698},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Watkins, Dayan - 2005 - Q-learning.pdf:pdf},
issn = {0885-6125},
journal = {Machine Learning},
month = {may},
number = {3-4},
pages = {279--292},
publisher = {Kluwer Academic Publishers},
title = {{Q-learning}},
url = {http://link.springer.com/10.1007/BF00992698},
volume = {8},
year = {2005}
}
@article{Zhou2019,
abstract = {Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1{\%} AP at 142 FPS, 37.4{\%} AP at 52 FPS, and 45.1{\%} AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.},
archivePrefix = {arXiv},
arxivId = {1904.07850},
author = {Zhou, Xingyi and Wang, Dequan and Kr{\"{a}}henb{\"{u}}hl, Philipp},
eprint = {1904.07850},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Zhou, Wang, Kr{\"{a}}henb{\"{u}}hl - 2019 - Objects as Points.pdf:pdf},
month = {apr},
title = {{Objects as Points}},
url = {http://arxiv.org/abs/1904.07850},
year = {2019}
}
@techreport{Kawaharazuka2019,
abstract = {For dynamic manipulation of flexible objects, we propose an acquisition method of a flexible object motion equation model using a deep neural network and a control method to realize a target state by calculating an optimized time-series joint torque command. By using the proposed method, any physics model of a target object is not needed, and the object can be controlled as intended. We applied this method to manipulations of a rigid object, a flexible object with and without environmental contact, and a cloth, and verified its effectiveness.},
archivePrefix = {arXiv},
arxivId = {1901.10142},
author = {Kawaharazuka, Kento and Ogawa, Toru and Tamura, Juntaro and Nabeshima, Cota},
eprint = {1901.10142},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Kawaharazuka et al. - Unknown - Dynamic Manipulation of Flexible Objects with Torque Sequence Using a Deep Neural Network.pdf:pdf},
title = {{Dynamic Manipulation of Flexible Objects with Torque Sequence Using a Deep Neural Network}},
url = {http://arxiv.org/abs/1901.10142},
year = {2019}
}
@techreport{Arakawa2018,
abstract = {Exploration has been one of the greatest challenges in reinforcement learning (RL), which is a large obstacle in the application of RL to robotics. Even with state-of-the-art RL algorithms, building a well-learned agent often requires too many trials, mainly due to the difficulty of matching its actions with rewards in the distant future. A remedy for this is to train an agent with real-time feedback from a human observer who immediately gives rewards for some actions. This study tackles a series of challenges for introducing such a human-in-the-loop RL scheme. The first contribution of this work is our experiments with a precisely modeled human observer: binary, delay, stochasticity, unsustainability, and natural reaction. We also propose an RL method called DQN-TAMER, which efficiently uses both human feedback and distant rewards. We find that DQN-TAMER agents outperform their baselines in Maze and Taxi simulated environments. Furthermore, we demonstrate a real-world human-in-the-loop RL application where a camera automatically recognizes a user's facial expressions as feedback to the agent while the agent explores a maze.},
archivePrefix = {arXiv},
arxivId = {1810.11748},
author = {Arakawa, Riku and Kobayashi, Sosuke and Unno, Yuya and Tsuboi, Yuta and Maeda, Shin-ichi},
eprint = {1810.11748},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Arakawa et al. - Unknown - DQN-TAMER Human-in-the-Loop Reinforcement Learning with Intractable Feedback.pdf:pdf},
title = {{DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback}},
url = {http://arxiv.org/abs/1810.11748},
year = {2018}
}
@article{Horiuchi2017,
author = {Horiuchi, Yuuki and Makino, Yasutoshi and Shinoda, Hiroyuki},
doi = {10.1145/3132272.3135076},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2017{\_}Computational Foresight Forecasting Human Body Motion in Real-time for Reducing Delays in Interactive System{\_}Horiuchi, Makino, Shin.pdf:pdf},
isbn = {9781450346917},
journal = {Proceedings of the Interactive Surfaces and Spaces, {\{}ISS{\}} 2017, Brighton, United Kingdom, October 17 - 20, 2017},
pages = {312--317},
title = {{Computational Foresight: Forecasting Human Body Motion in Real-time for Reducing Delays in Interactive System}},
url = {https://doi.org/10.1145/3132272.3135076},
year = {2017}
}
@article{Silver2016,
abstract = {The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8{\%} winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.},
author = {Silver, David and Huang, Aja and Maddison, Chris J and Guez, Arthur and Sifre, Laurent and Driessche, George Van Den and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and Kavukcuoglu, Koray and van den Driessche, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and Kavukcuoglu, Koray and Graepel, Thore and Hassabis, Demis},
doi = {10.1038/nature16961},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Mastering the game of Go with deep neural networks and tree search.{\_}Silver et al.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Mastering the game of Go with deep neural networks and tree search.{\_}Silver et al.pdf:pdf},
issn = {1476-4687},
journal = {Nature},
number = {7587},
pages = {484--9},
pmid = {26819042},
publisher = {Nature Publishing Group},
title = {{Mastering the game of Go with deep neural networks and tree search.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26819042},
volume = {529},
year = {2016}
}
@article{Mnih2013a,
abstract = {We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.},
archivePrefix = {arXiv},
arxivId = {1312.5602},
author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Graves, Alex and Antonoglou, Ioannis and Wierstra, Daan and Riedmiller, Martin},
eprint = {1312.5602},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2013{\_}Playing Atari with Deep Reinforcement Learning{\_}Mnih et al.pdf:pdf},
pages = {1--9},
title = {{Playing Atari with Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1312.5602},
year = {2013}
}
@article{Gu2017b,
abstract = {Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.},
author = {Gu, Shixiang and Holly, Ethan and Lillicrap, Timothy and Levine, Sergey and {Venkatraman Narayanan}, Maxim Likhachev},
doi = {10.1109/ICRA.2017.7989385},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2017{\_}Deep Reinforcement Learning for Robotic Manipulation{\_}Gu et al.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates{\_}Gu et al.pdf:pdf;:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Venkatraman Narayanan - 2017 - Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates Shixiang.pdf:pdf},
isbn = {9781509046331},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
keywords = {Automatisierungstechnik,Computer Vision for Automa},
pages = {3389--3396},
title = {{Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates}},
url = {http://ieeexplore.ieee.org/servlet/opac?punumber=7960754},
year = {2017}
}
@article{Xiao2018,
abstract = {Figure 1. Our self-contained projection smartwatch (A) provides rectified graphics with touch input on the skin (B). We use a slide-to-unlock mechanism to reject inadvertent touches and provide a rapid projection calibration (C) before apps can be used (D). ABSTRACT Compact, worn computers with projected, on-skin touch interfaces have been a long-standing yet elusive goal, largely written off as science fiction. Such devices offer the potential to mitigate the significant human input/output bottleneck inherent in worn devices with small screens. In this work, we present the first, fully-functional and self-contained projection smartwatch implementation, containing the requisite compute, power, projection and touch-sensing capabilities. Our watch offers roughly 40 cm 2 of interactive surface area-more than five times that of a typical smartwatch display. We demonstrate continuous 2D finger tracking with interactive, rectified graphics, transforming the arm into a touchscreen. We discuss our hardware and software implementation, as well as evaluation results regarding touch accuracy and projection visibility.},
author = {Xiao, Robert and Cao, Teng and Guo, Ning and Zhuo, Jun and Zhang, Yang and Harrison, Chris},
doi = {10.1145/3173574.3173669},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}LumiWatch On-Arm Projected Graphics and Touch Input{\_}Xiao et al.pdf:pdf},
isbn = {9781450356206},
journal = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18},
keywords = {Author's kit,Conference Publications,Guides,depth sensing,instructions,on-body interaction,projection,smartwatch,time-of-flight,touch interaction},
pages = {1--11},
title = {{LumiWatch: On-Arm Projected Graphics and Touch Input}},
url = {https://doi.org/10.1145/3173574.3173669},
year = {2018}
}
@article{Lv2015a,
abstract = {There is an increasing interest in creating pervasive games based on emerging interaction technologies. In order to develop touch-less, interactive and augmented reality games on vision-based wearable device, a touch-less motion interaction technology is designed and evaluated in this work. Users interact with the augmented reality games with dynamic hands/feet gestures in front of the camera, which triggers the interaction event to interact with the virtual object in the scene. Three primitive augmented reality games with eleven dynamic gestures are developed based on the proposed touch-less interaction technology as proof. At last, a comparing evaluation is proposed to demonstrate the social acceptability and usability of the touch-less approach, running on a hybrid wearable framework or with Google Glass, as well as workload assessment, user's emotions and satisfaction.},
author = {Lv, Zhihan and Halawani, Alaa and Feng, Shengzhong and {Ur R{\'{e}}hman}, Shafiq and Li, Haibo},
doi = {10.1007/s00779-015-0844-1},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2015{\_}Touch-less interactive augmented reality game on vision-based wearable device{\_}Lv et al.pdf:pdf},
issn = {16174909},
journal = {Personal and Ubiquitous Computing},
keywords = {Augmented reality game,Hand free,Pervasive game,Smartphone game,Touch-less,Wearable device,game {\'{a}},game {\'{a}} augmented reality,hand free {\'{a}} pervasive,wearable device {\'{a}} smartphone},
number = {3},
pages = {551--567},
title = {{Touch-less interactive augmented reality game on vision-based wearable device}},
volume = {19},
year = {2015}
}
@article{Cvpr2019a,
abstract = {Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors. Many WSOD approaches adopt multiple instance learning (MIL) and have non-convex loss functions which are prone to get stuck into local minima (falsely localize object parts) while missing full object extent during training. In this paper, we introduce a continuation optimization method into MIL and thereby creating continuation multiple instance learning (C-MIL), with the intention of alleviating the non-convexity problem in a systematic way. We partition instances into spatially related and class related subsets, and approximate the original loss function with a series of smoothed loss functions defined within the subsets. Optimizing smoothed loss functions prevents the training procedure falling prematurely into local minima and facilitates the discovery of Stable Semantic Extremal Regions (SSERs) which indicate full object extent. On the PASCAL VOC 2007 and 2012 datasets, C-MIL improves the state-of-the-art of weakly supervised object detection and weakly supervised object localization with large margins.},
archivePrefix = {arXiv},
arxivId = {1904.05647},
author = {Wan, Fang and Liu, Chang and Ke, Wei and Ji, Xiangyang and Jiao, Jianbin and Ye, Qixiang},
eprint = {1904.05647},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}C-MIL Continuation Multiple Instance Learning for Weakly Supervised Object Detection{\_}Cvpr, Id(2).pdf:pdf},
month = {apr},
title = {{C-MIL : Continuation Multiple Instance Learning for Weakly Supervised Object Detection}},
url = {http://arxiv.org/abs/1904.05647},
year = {2019}
}
@article{Lee2009,
abstract = {This paper proposes an interactive e-learning system using pattern recognition and augmented reality. The goal of proposed system is to provide students with realistic audio-visual contents when they are leaning. The proposed e- learning system consists of image recognition, color and polka-dot pattern recognition, and augmented reality engine with audio-visual contents. When the web camera on a PC captures the current page of textbook, the e-Iearning system first identifies the images on the page, and augments some audio-visual contents on the monitor. For interactive learning, the proposed e-learning system exploits the color-band or polka-dot markers which are stuck to the end of a finger. The color-band and polka-dot marker act like the mouse cursor to indicate the position in the textbook image. Appropriate interactive audio-visual contents are augmented as the marker is located on the predefined image objects in the textbook. The proposed e-learning system was applied to the edu},
author = {{Sang Hwa}, Lee and Junyeong, Choi and Jong-Il, Park and Lee, Sang Hwa and Choi, Junyeong and Park, Jong Il and {Sang Hwa}, Lee and Junyeong, Choi and Jong-Il, Park},
doi = {10.1109/TCE.2009.5174470},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Lee et al. - 2009 - Interactive E-Learning System Using Pattern Recognition and Augmented Reality(3).pdf:pdf;:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Lee et al. - 2009 - Interactive E-Learning System Using Pattern Recognition and Augmented Reality(2).pdf:pdf},
issn = {00983063},
journal = {IEEE Transactions on Consumer Electronics},
keywords = {AUDIOVISUAL education,Augmented reality,COMPUTER network resources,E-learning system,INTERACTIVE multimedia,Interactive learning,PATTERN perception,Pattern recognition,STUDENTS -- Services for,WEB-based instruction,augmented reality,interactive learning,pattern recognition},
number = {2},
pages = {883--890},
title = {{Interactive E-Learning System Using Pattern Recognition and Augmented Reality.}},
url = {http://search-1ebscohost-1com-1ebsco.han.buw.uw.edu.pl/login.aspx?direct=true{\&}db=a9h{\&}AN=43924116{\&}lang=pl{\&}site=ehost-live},
volume = {55},
year = {2009}
}
@article{Mousavi2018c,
abstract = {In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This chapter reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.},
archivePrefix = {arXiv},
arxivId = {arXiv:1701.07274v6},
author = {Mousavi, Seyed Sajad and Schukat, Michael and Howley, Enda},
doi = {10.1007/978-3-319-56991-8_32},
eprint = {arXiv:1701.07274v6},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Deep Reinforcement Learning An Overview{\_}Mousavi, Schukat, Howley.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Deep Reinforcement Learning An Overview{\_}Mousavi, Schukat, Howley.pdf:pdf},
issn = {23673389},
journal = {Lecture Notes in Networks and Systems},
keywords = {Deep leaning,MDPs,Neural networks,Observable MDPs,Reinforcement learning},
pages = {426--440},
title = {{Deep Reinforcement Learning: An Overview}},
volume = {16},
year = {2018}
}
@article{Denil2016a,
abstract = {When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way. This process of active interaction is in the same spirit as a scientist performing experiments to discover hidden facts. Recent advances in artificial intelligence have yielded machines that can achieve superhuman performance in Go, Atari, natural language processing, and complex control problems; however, it is not clear that these systems can rival the scientific intuition of even a young child. In this work we introduce a basic set of tasks that require agents to estimate properties such as mass and cohesion of objects in an interactive simulated environment where they can manipulate the objects and observe the consequences. We found that state of art deep reinforcement learning methods can learn to perform the experiments necessary to discover such hidden properties. By systematically manipulating the problem difficulty and the cost incurred by the agent for performing experiments, we found that agents learn different strategies that balance the cost of gathering information against the cost of making mistakes in different situations.},
archivePrefix = {arXiv},
arxivId = {1611.01843},
author = {Denil, Misha and Agrawal, Pulkit and Kulkarni, Tejas D and Erez, Tom and Battaglia, Peter and de Freitas, Nando},
eprint = {1611.01843},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Learning to Perform Physics Experiments via Deep Reinforcement Learning{\_}Denil et al.pdf:pdf},
pages = {1--15},
title = {{Learning to Perform Physics Experiments via Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1611.01843},
year = {2016}
}
@article{Chamzas2019a,
abstract = {Sampling-based planners are effective in many real-world applications such as robotics manipulation, navigation, and even protein modeling. However, it is often challenging to generate a collision-free path in environments where key areas are hard to sample. In the absence of any prior information, sampling-based planners are forced to explore uniformly or heuristically, which can lead to degraded performance. One way to improve performance is to use prior knowledge of environments to adapt the sampling strategy to the problem at hand. In this work, we decompose the workspace into local primitives, memorizing local experiences by these primitives in the form of local samplers, and store them in a database. We synthesize an efficient global sampler by retrieving local experiences relevant to the given situation. Our method transfers knowledge effectively between diverse environments that share local primitives and speeds up the performance dramatically. Our results show, in terms of solution time, an improvement of multiple orders of magnitude in two traditionally challenging high-dimensional problems compared to state-of-the-art approaches.},
archivePrefix = {arXiv},
arxivId = {1903.08693},
author = {Chamzas, Constantinos and Shrivastava, Anshumali and Kavraki, Lydia E.},
eprint = {1903.08693},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2019{\_}Using Local Experiences for Global Motion Planning{\_}Chamzas, Shrivastava, Kavraki.pdf:pdf},
month = {mar},
title = {{Using Local Experiences for Global Motion Planning}},
url = {http://arxiv.org/abs/1903.08693},
year = {2019}
}
@article{Hu2018,
abstract = {Most methods for object instance segmentation require all training examples to be labeled with segmentation masks. This requirement makes it expensive to annotate new categories and has restricted instance segmentation models to {\~{}}100 well-annotated classes. The goal of this paper is to propose a new partially supervised training paradigm, together with a novel weight transfer function, that enables training instance segmentation models on a large set of categories all of which have box annotations, but only a small fraction of which have mask annotations. These contributions allow us to train Mask R-CNN to detect and segment 3000 visual concepts using box annotations from the Visual Genome dataset and mask annotations from the 80 classes in the COCO dataset. We evaluate our approach in a controlled study on the COCO dataset. This work is a first step towards instance segmentation models that have broad comprehension of the visual world.},
archivePrefix = {arXiv},
arxivId = {arXiv:1711.10370v2},
author = {Hu, Ronghang and Dollar, Piotr and He, Kaiming and Darrell, Trevor and Girshick, Ross},
doi = {10.1109/CVPR.2018.00445},
eprint = {arXiv:1711.10370v2},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Learning to Segment Every Thing{\_}Hu et al.pdf:pdf},
isbn = {9781538664209},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {4233--4241},
title = {{Learning to Segment Every Thing}},
year = {2018}
}
@article{Levine2015b,
abstract = {Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a partially observed guided policy

repository: yumion/master-thesis

repo_name: yumion/master-thesis