text:
search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.},
archivePrefix = {arXiv},
arxivId = {1504.00702},
author = {Levine, Sergey and Finn, Chelsea and Darrell, Trevor and Abbeel, Pieter},
eprint = {1504.00702},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}End-to-End Training of Deep Visuomotor Policies{\_}Levine et al.pdf:pdf},
keywords = {neural networks,optimal control,reinforcement learning,vision},
pages = {1--40},
title = {{End-to-End Training of Deep Visuomotor Policies}},
url = {http://arxiv.org/abs/1504.00702},
volume = {17},
year = {2015}
}
@article{Finn2016b,
abstract = {A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment. Many existing methods for learning the dynamics of physical interactions require labeled object information. However, to scale real-world interaction learning to a variety of scenes and objects, acquiring labeled data becomes increasingly impractical. To learn about physical object motion without labels, we develop an action-conditioned video prediction model that explicitly models pixel motion, by predicting a distribution over pixel motion from previous frames. Because our model explicitly predicts motion, it is partially invariant to object appearance, enabling it to generalize to previously unseen objects. To explore video prediction for real-world interactive agents, we also introduce a dataset of 59,000 robot interactions involving pushing motions, including a test set with novel objects. In this dataset, accurate prediction of videos conditioned on the robot's future actions amounts to learning a "visual imagination" of different futures based on different courses of action. Our experiments show that our proposed method produces more accurate video predictions both quantitatively and qualitatively, when compared to prior methods.},
archivePrefix = {arXiv},
arxivId = {1605.07157},
author = {Finn, Chelsea and Goodfellow, Ian and Levine, Sergey},
eprint = {1605.07157},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Unsupervised Learning for Physical Interaction through Video Prediction{\_}Finn, Goodfellow, Levine.pdf:pdf},
title = {{Unsupervised Learning for Physical Interaction through Video Prediction}},
url = {http://arxiv.org/abs/1605.07157},
year = {2016}
}
@article{CanonBermudez2018,
abstract = {Electronic skins equipped with artificial receptors are able to extend our perception beyond the modalities that have naturally evolved. These synthetic receptors offer complimentary information on our surroundings and endow us with novel means of manipulating physical or even virtual objects. We realize highly compliant magnetosensitive skins with directional perception that enable magnetic cognition, body position tracking, and touchless object manipulation. Transfer printing of eight high-performance spin valve sensors arranged into two Wheatstone bridges onto 1.7-$\mu$m-thick polyimide foils ensures mechanical imperceptibility. This resembles a new class of interactive devices extracting information from the surroundings through magnetic tags. We demonstrate this concept in augmented reality systems with virtual knob-turning functions and the operation of virtual dialing pads, based on the interaction with magnetic fields. This technology will enable a cornucopia of applications from navigation, motion tracking in robotics, regenerative medicine, and sports and gaming to interaction in supplemented reality.},
author = {{Ca{\~{n}}{\'{o}}n Berm{\'{u}}dez}, Gilbert Santiago and Karnaushenko, Dmitriy Daniil Dmitriy D. and Karnaushenko, Dmitriy Daniil Dmitriy D. and Lebanov, Ana and Bischoff, Lothar and Kaltenbrunner, Martin and Fassbender, J{\"{u}}rgen and Schmidt, Oliver G. and Makarov, Denys and Santiago, Gilbert and Berm{\'{u}}dez, Ca{\~{n}}{\'{o}}n and Karnaushenko, Dmitriy Daniil Dmitriy D. and Karnaushenko, Dmitriy Daniil Dmitriy D. and Lebanov, Ana and Bischoff, Lothar and Kaltenbrunner, Martin and Fassbender, J{\"{u}}rgen and Schmidt, Oliver G. and Makarov, Denys},
doi = {10.1126/sciadv.aao2623},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Magnetosensitive e-skins with directional perception for augmented reality{\_}Ca{\~{n}}{\'{o}}n Berm{\'{u}}dez et al.pdf:pdf},
issn = {23752548},
journal = {Science Advances},
number = {1},
title = {{Magnetosensitive e-skins with directional perception for augmented reality}},
volume = {4},
year = {2018}
}
@article{Tao2019a,
abstract = {Current deep neural networks suffer from two problems; first, they are hard to interpret, and second, they suffer from overfitting. There have been many attempts to define interpretability in neural networks, but they typically lack causality or generality. A myriad of regularization techniques have been developed to prevent overfitting, and this has driven deep learning to become the hot topic it is today; however, while most regularization techniques are justified empirically and even intuitively, there is not much underlying theory. This paper argues that to extract the features used in neural networks to make decisions, it's important to look at the paths between clusters existing in the hidden spaces of neural networks. These features are of particular interest because they reflect the true decision making process of the neural network. This analysis is then furthered to present an ensemble algorithm for arbitrary neural networks which has guarantees for test accuracy. Finally, a discussion detailing the aforementioned guarantees is introduced and the implications to neural networks, including an intuitive explanation for all current regularization methods, are presented. The ensemble algorithm has generated state-of-the-art results for Wide-ResNet on CIFAR-10 and has improved test accuracy for all models it has been applied to.},
archivePrefix = {arXiv},
arxivId = {1904.05488},
author = {Tao, Sean},
eprint = {1904.05488},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Deep Neural Network Ensembles{\_}Tao.pdf:pdf},
month = {apr},
title = {{Deep Neural Network Ensembles}},
url = {http://arxiv.org/abs/1904.05488},
year = {2019}
}
@article{Mnih2016,
abstract = {We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.},
archivePrefix = {arXiv},
arxivId = {1602.01783},
author = {Mnih, Volodymyr and Mirza, Mehdi and Graves, Alex and Harley, Tim and Lillicrap, Timothy P. and Silver, David and Com, Korayk Google and Badia, Adri{\`{a}} Puigdom{\`{e}}nech and Mirza, Mehdi and Graves, Alex and Lillicrap, Timothy P. and Harley, Tim and Silver, David and Kavukcuoglu, Koray},
eprint = {1602.01783},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Asynchronous Methods for Deep Reinforcement Learning{\_}Mnih et al.pdf:pdf},
title = {{Asynchronous Methods for Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1602.01783},
volume = {48},
year = {2016}
}
@article{James2018a,
abstract = {Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation in order to produce large amounts of labelled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this "reality gap" requires at best a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power, rendering certain reinforcement learning (RL) methods unable to learn the task of interest. In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images. We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70{\%} zero-shot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91{\%}, outperforming a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99{\%}.},
archivePrefix = {arXiv},
arxivId = {1812.07252},
author = {James, Stephen and Wohlhart, Paul and Kalakrishnan, Mrinal and Kalashnikov, Dmitry and Irpan, Alex and Ibarz, Julian and Levine, Sergey and Hadsell, Raia and Bousmalis, Konstantinos},
eprint = {1812.07252},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/James et al. - 2018 - Sim-to-Real via Sim-to-Sim Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Sim-to-Real via Sim-to-Sim Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks{\_}James et al.pdf:pdf},
month = {dec},
title = {{Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks}},
url = {http://arxiv.org/abs/1812.07252},
year = {2018}
}
@article{He2017a,
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 bells and whistles, 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 has been made available at: https://github.com/facebookresearch/Detectron},
author = {He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross},
doi = {10.1109/ICCV.2017.322},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/He et al. - 2017 - Mask R-CNN.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Mask R-CNN{\_}He et al.pdf:pdf},
isbn = {9781538610329},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
month = {oct},
pages = {2980--2988},
publisher = {IEEE},
title = {{Mask R-CNN}},
url = {http://ieeexplore.ieee.org/document/8237584/},
volume = {2017-Octob},
year = {2017}
}
@article{Lee2014a,
abstract = {Ultrathin piezoelectric nanogenerator (NG) with a total thickness of ≈16 $\mu$m is fabricated as an active or self-powered sensor for monitoring local deformation on a human skin. The NG was based on an anodic aluminum oxide (AAO) as an insulating layer grown on a thin Al foil by anodization, on which a thin film made of aligned ZnO nanowire compacted arrays is grown by solution chemistry. The performance of the NG is characterized with the assistance of the finite element method (FEM) simulation. The extremely thin NG is attached on the surface of an eyelid, and its output voltage/current characterizes the motion of the eye ball underneath. Since there is no external power needed for the operation of the NG, this self-powered or active sensor can be effective in monitoring sleeping behavior, brain activities, and spirit status of a person as well as any biological associated skin deformation},
author = {Lee, Sangmin and Hinchet, Ronan and Lee, Yean and Yang, Ya and Lin, Zong-hong Hong and Ardila, Gustavo and Mont{\`{e}}s, Laurent and Mouis, Mireille and Wang, Zhong Lin},
doi = {10.1002/adfm.201301971},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2014{\_}Ultrathin nanogenerators as self-poweredactive skin sensors for tracking eye ball motion{\_}Lee et al.pdf:pdf},
issn = {1616301X},
journal = {Advanced Functional Materials},
keywords = {active sensors,eye ball motion,self-powered systems,ultrathin nanogenerators},
number = {8},
pages = {1163--1168},
title = {{Ultrathin nanogenerators as self-powered/active skin sensors for tracking eye ball motion}},
volume = {24},
year = {2014}
}
@article{Jang2018a,
abstract = {Well structured visual representations can make robot learning faster and can improve generalization. In this paper, we study how we can acquire effective object-centric representations for robotic manipulation tasks without human labeling by using autonomous robot interaction with the environment. Such representation learning methods can benefit from continuous refinement of the representation as the robot collects more experience, allowing them to scale effectively without human intervention. Our representation learning approach is based on object persistence: when a robot removes an object from a scene, the representation of that scene should change according to the features of the object that was removed. We formulate an arithmetic relationship between feature vectors from this observation, and use it to learn a representation of scenes and objects that can then be used to identify object instances, localize them in the scene, and perform goal-directed grasping tasks where the robot must retrieve commanded objects from a bin. The same grasping procedure can also be used to automatically collect training data for our method, by recording images of scenes, grasping and removing an object, and recording the outcome. Our experiments demonstrate that this self-supervised approach for tasked grasping substantially outperforms direct reinforcement learning from images and prior representation learning methods.},
archivePrefix = {arXiv},
arxivId = {1811.06964},
author = {Jang, Eric and Devin, Coline and Vanhoucke, Vincent and Levine, Sergey},
eprint = {1811.06964},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Jang et al. - 2018 - Grasp2Vec Learning Object Representations from Self-Supervised Grasping(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Grasp2Vec Learning Object Representations from Self-Supervised Grasping{\_}Jang et al.pdf:pdf},
keywords = {instance grasping,reinforcement learning,unsupervised learning},
number = {CoRL},
pages = {1--15},
title = {{Grasp2Vec: Learning Object Representations from Self-Supervised Grasping}},
url = {http://arxiv.org/abs/1811.06964},
year = {2018}
}
@article{Hausknecht2015,
abstract = {Deep Reinforcement Learning has yielded proficient controllers for complex tasks. However, these controllers have limited memory and rely on being able to perceive the complete game screen at each decision point. To address these shortcomings, this article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected layer with a recurrent LSTM. The resulting $\backslash$textit{\{}Deep Recurrent Q-Network{\}} (DRQN), although capable of seeing only a single frame at each timestep, successfully integrates information through time and replicates DQN's performance on standard Atari games and partially observed equivalents featuring flickering game screens. Additionally, when trained with partial observations and evaluated with incrementally more complete observations, DRQN's performance scales as a function of observability. Conversely, when trained with full observations and evaluated with partial observations, DRQN's performance degrades less than DQN's. Thus, given the same length of history, recurrency is a viable alternative to stacking a history of frames in the DQN's input layer and while recurrency confers no systematic advantage when learning to play the game, the recurrent net can better adapt at evaluation time if the quality of observations changes.},
archivePrefix = {arXiv},
arxivId = {1507.06527},
author = {Hausknecht, Matthew and Stone, Peter},
eprint = {1507.06527},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}Deep Recurrent Q-Learning for Partially Observable MDPs{\_}Hausknecht, Stone.pdf:pdf},
keywords = {Technical Report FS-15-06},
pages = {29--37},
title = {{Deep Recurrent Q-Learning for Partially Observable MDPs}},
url = {http://arxiv.org/abs/1507.06527},
year = {2015}
}
@article{Matas2018a,
abstract = {We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their applicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world. To-date, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are to be employed further, then it should be possible to learn to interact with a wide variety of objects, and not only rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating deformable objects (specifically cloth). We evaluate our approach on three tasks --- folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects.},
archivePrefix = {arXiv},
arxivId = {1806.07851},
author = {Matas, Jan and James, Stephen and Davison, Andrew J.},
eprint = {1806.07851},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Matas, James, Davison - 2018 - Sim-to-Real Reinforcement Learning for Deformable Object Manipulation.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Sim-to-Real Reinforcement Learning for Deformable Object Manipulation{\_}Matas, James, Davison.pdf:pdf},
keywords = {deformable objects,manipulation,reinforcement learning},
number = {CoRL},
title = {{Sim-to-Real Reinforcement Learning for Deformable Object Manipulation}},
url = {http://arxiv.org/abs/1806.07851},
year = {2018}
}
@article{Li2018,
abstract = {For the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of prosthetic hand will be greatly improved if the stable grip of prosthetic hand can be achieved. To address this problem, in this study, a bio-signal control method for grasping control of a prosthetic hand is proposed to improve patient's sense of using prosthetic hand and the thus improving the quality of life. A MYO gesture control armband is used to collect the surface electromyographic (sEMG) signals from the upper limb. The overlapping sliding window scheme are applied for data segmentation and the correlated features are extracted from each segmented data. Principal component analysis (PCA) methods are then deployed for dimension reduction. Deep neural network is used to generate sEMG-force regression model for force prediction at different levels. The predicted force values are input to a fuzzy controller for the grasping control of a prosthetic hand. A vibration feedback device is used to feed grasping force value back to patient's arm to improve patient's sense of using prosthetic hand and realize accurate grasping. To test the effectiveness of the scheme, 15 able-bodied subjects participated in the experiments. The classification results indicated that 8-channel sEMG applying all four time-domain features, with PCA reduction from 32 to 8 dimensions results in the highest classification accuracy. Based on the experimental results from 15 participants, the average recognition rate is over 95{\%}. On the other hand, from the statistical results of standard deviation, the between-subject variations ranges from 3.58 to 1.25{\%}, proving that the robustness and stability of the proposed approach. The method proposed hereto control grasping power through the patient's own sEMG signal, which achieves a high recognition rate to improve the success rate of grip and increases the sense of operation and also brings the gospel for upper extremity amputation patients.},
author = {Li, Chuanjiang and Ren, Jian and Huang, Huaiqi and Wang, Bin and Zhu, Yanfei and Hu, Huosheng},
doi = {10.1186/s12938-018-0539-8},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}PCA and deep learning based myoelectric grasping control of a prosthetic hand{\_}Li et al.pdf:pdf},
issn = {1475925X},
journal = {BioMedical Engineering Online},
keywords = {DNN,Fuzzy controller,Grasp control,PCA,Prosthetic hand,SEMG-force,Vibration feedback device},
number = {1},
pages = {1--18},
publisher = {BioMed Central},
title = {{PCA and deep learning based myoelectric grasping control of a prosthetic hand}},
url = {https://doi.org/10.1186/s12938-018-0539-8},
volume = {17},
year = {2018}
}
@article{Karambakhsh2019,
abstract = {Augmented reality is very useful in medical education because of the problem of having body organs in a regular classroom. In this paper, we propose to apply augmented reality to improve the way of teaching in medical schools and institutes. We propose a novel convolutional neural network (CNN) for gesture recognition, which recognizes the human's gestures as a certain instruction. We use augmented reality technology for anatomy learning, which simulates the scenarios where students can learn Anatomy with HoloLens instead of rare specimens. We have used the mesh reconstruction to reconstruct the 3D specimens. A user interface featured augment reality has been designed which fits the common process of anatomy learning. To improve the interaction services, we have applied gestures as an input source and improve the accuracy of gestures recognition by an updated deep convolutional neural network. Our proposed learning method includes many separated train procedures using cloud computing. Each train model and its related inputs have been sent to our cloud and the results are returned to the server. The suggested cloud includes windows and android devices, which are able to install deep convolutional learning libraries. Compared with previous gesture recognition, our approach is not only more accurate but also has more potential for adding new gestures. Furthermore, we have shown that neural networks can be combined with augmented reality as a rising field, and the great potential of augmented reality and neural networks to be employed for medical learning and education systems.},
author = {Karambakhsh, Ahmad and Kamel, Aouaidjia and Sheng, Bin and Li, Ping and Yang, Po and Dagan, David and Feng, David Dagan},
doi = {10.1016/j.ijinfomgt.2018.03.004},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Karambakhsh et al. - 2019 - Deep gesture interaction for augmented anatomy learning(2).pdf:pdf},
issn = {02684012},
journal = {International Journal of Information Management},
keywords = {3D reconstruction,Augmented reality,Medical education,Mobile cloud,Neural network},
pages = {328--336},
publisher = {Elsevier},
title = {{Deep gesture interaction for augmented anatomy learning}},
url = {https://doi.org/10.1016/j.ijinfomgt.2018.03.004},
volume = {45},
year = {2018}
}
@article{Hafner2018a,
abstract = {Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from interactions with the world. However, learning dynamics models that are accurate enough for planning has been a long-standing challenge, especially in image-based domains. We propose the Deep Planning Network (PlaNet), a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space. To achieve high performance, the dynamics model must accurately predict the rewards ahead for multiple time steps. We approach this problem using a latent dynamics model with both deterministic and stochastic transition components and a multi-step variational inference objective that we call latent overshooting. Using only pixel observations, our agent solves continuous control tasks with contact dynamics, partial observability, and sparse rewards, which exceed the difficulty of tasks that were previously solved by planning with learned models. PlaNet uses substantially fewer episodes and reaches final performance close to and sometimes higher than strong model-free algorithms.},
archivePrefix = {arXiv},
arxivId = {1811.04551},
author = {Hafner, Danijar and Lillicrap, Timothy and Fischer, Ian and Villegas, Ruben and Ha, David and Lee, Honglak and Davidson, James},
eprint = {1811.04551},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Learning Latent Dynamics for Planning from Pixels{\_}Hafner et al.pdf:pdf},
month = {nov},
title = {{Learning Latent Dynamics for Planning from Pixels}},
url = {http://arxiv.org/abs/1811.04551},
year = {2018}
}
@article{Lample2016,
abstract = {Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. In this paper, we present the first architecture to tackle 3D environments in first-person shooter games, that involve partially observable states. Typically, deep reinforcement learning methods only utilize visual input for training. We present a method to augment these models to exploit game feature information such as the presence of enemies or items, during the training phase. Our model is trained to simultaneously learn these features along with minimizing a Q-learning objective, which is shown to dramatically improve the training speed and performance of our agent. Our architecture is also modularized to allow different models to be independently trained for different phases of the game. We show that the proposed architecture substantially outperforms built-in AI agents of the game as well as humans in deathmatch scenarios.},
archivePrefix = {arXiv},
arxivId = {1609.05521},
author = {Lample, Guillaume and Chaplot, Devendra Singh},
eprint = {1609.05521},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Playing FPS Games with Deep Reinforcement Learning{\_}Lample, Chaplot.pdf:pdf},
keywords = {Machine Learning Methods},
pages = {2140--2146},
title = {{Playing FPS Games with Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1609.05521},
year = {2016}
}
@article{Wang2018,
abstract = {Although cost-effective at-home blood pressure monitors are available, a complementary mobile solution can ease the bur-den of measuring BP at critical points throughout the day. In this work, we developed and evaluated a smartphone-based BP monitoring application called Seismo. The technique re-lies on measuring the time between the opening of the aortic valve and the pulse later reaching a periphery arterial site. It uses the smartphone's accelerometer to measure the vibration caused by the heart valve movements and the smartphone's camera to measure the pulse at the fingertip. The system was evaluated in a nine participant longitudinal BP perturbation study. Each participant participated in four sessions that in-volved stationary biking at multiple intensities. The Pearson correlation coefficient of the blood pressure estimation across participants is 0.20-0.77 (µ=0.55, $\sigma$ =0.19), with an RMSE of 3.3-9.2 mmHg (µ=5.2, $\sigma$ =2.0).},
author = {Wang, Edward Jay and Zhu, Junyi and Jain, Mohit and Lee, Tien-jui and Saba, Elliot and Nachman, Lama and Patel, Shwetak N},
doi = {10.1145/3173574.3173999},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Seismo Blood Pressure Monitoring using Built-in Smartphone Accelerometer and Camera{\_}Wang et al.pdf:pdf},
isbn = {9781450356206},
journal = {Chi '18},
keywords = {Applications Author Keywords Physiological sensing,I54 Image Processing and Computer Vision,Miscellaneous,PPG,PTT,SCG,noninvasive blood pressure,photoplethysmography,pulse transit time,seismocardiography},
pages = {425},
title = {{Seismo: Blood Pressure Monitoring using Built-in Smartphone Accelerometer and Camera}},
url = {https://dl.acm.org/citation.cfm?id=3173999},
year = {2018}
}
@article{Zhang2019,
abstract = {In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, we seek to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as 1) no extra transfer steps are required during the expensive training of DRL agents in simulation; 2) the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; 3) the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a simple yet effective shift loss that is agnostic to the downstream task, to constrain the consistency between subsequent frames which is important for consistent policy outputs. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in indoor and outdoor robotics experiments.},
archivePrefix = {arXiv},
arxivId = {1802.00265},
author = {Zhang, Jingwei and Tai, Lei and Yun, Peng and Xiong, Yufeng and Liu, Ming and Boedecker, Joschka and Burgard, Wolfram},
doi = {10.1109/LRA.2019.2894216},
eprint = {1802.00265},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Zhang et al. - 2018 - VR-Goggles for Robots Real-to-sim Domain Adaptation for Visual Control.pdf:pdf;:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Zhang et al. - 2019 - VR-Goggles for Robots Real-to-Sim Domain Adaptation for Visual Control.pdf:pdf},
issn = {23773766},
journal = {IEEE Robotics and Automation Letters},
keywords = {Deep learning in robotics and automation,model learning for control,visual-based navigation},
number = {2},
pages = {1148--1155},
publisher = {IEEE},
title = {{VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control}},
url = {http://arxiv.org/abs/1802.00265},
volume = {4},
year = {2019}
}
@article{Silver2017a,
abstract = {Starting from zero knowledge and without human data, AlphaGo Zero was able to teach itself to play Go and to develop novel strategies that provide new insights into the oldest of games.},
author = {Silver, David and Schrittwieser, Julian and Simonyan, Karen and Antonoglou, Ioannis and Huang, Aja and Guez, Arthur and Hubert, Thomas and Baker, Lucas and Lai, Matthew and Bolton, Adrian and Chen, Yutian and Lillicrap, Timothy and Hui, Fan and Sifre, Laurent and {Van Den Driessche}, George and Graepel, Thore and Hassabis, Demis},
doi = {10.1038/nature24270},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2017{\_}Mastering the game of Go without human knowledge{\_}Silver et al.pdf:pdf},
issn = {14764687},
journal = {Nature},
number = {7676},
pages = {354--359},
publisher = {Nature Publishing Group},
title = {{Mastering the game of Go without human knowledge}},
url = {http://dx.doi.org/10.1038/nature24270},
volume = {550},
year = {2017}
}
@article{Isola2017b,
abstract = {We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.},
author = {Isola, Phillip and Zhu, Jun Yan and Zhou, Tinghui and Efros, Alexei A.},
doi = {10.1109/CVPR.2017.632},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Isola et al. - 2017 - Image-to-image translation with conditional adversarial networks.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Image-to-image translation with conditional adversarial networks{\_}Isola et al.pdf:pdf},
isbn = {9781538604571},
journal = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
pages = {5967--5976},
title = {{Image-to-image translation with conditional adversarial networks}},
volume = {2017-Janua},
year = {2017}
}
@article{Silver2017,
abstract = {The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case.},
archivePrefix = {arXiv},
arxivId = {1712.01815},
author = {Silver, David and Hubert, Thomas and Schrittwieser, Julian and Antonoglou, Ioannis and Lai, Matthew and Guez, Arthur and Lanctot, Marc and Sifre, Laurent and Kumaran, Dharshan and Graepel, Thore and Lillicrap, Timothy and Simonyan, Karen and Hassabis, Demis},
eprint = {1712.01815},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Silver et al. - 2017 - Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm{\_}Silver et al.pdf:pdf},
pages = {1--19},
title = {{Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm}},
url = {http://arxiv.org/abs/1712.01815},
year = {2017}
}
@article{Redmon2015a,
abstract = {We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques. The model outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU. Our network can simultaneously perform classification so that in a single step it recognizes the object and finds a good grasp rectangle. A modification to this model predicts multiple grasps per object by using a locally constrained prediction mechanism. The locally constrained model performs significantly better, especially on objects that can be grasped in a variety of ways.},
author = {Redmon, Joseph and Angelova, Anelia},
doi = {10.1109/ICRA.2015.7139361},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Redmon, Angelova - 2015 - Real-time grasp detection using convolutional neural networks.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}Real-time grasp detection using convolutional neural networks{\_}Redmon, Angelova.pdf:pdf},
isbn = {9781479969234},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
month = {may},
number = {June},
pages = {1316--1322},
publisher = {IEEE},
title = {{Real-time grasp detection using convolutional neural networks}},
url = {http://ieeexplore.ieee.org/document/7139361/},
volume = {2015-June},
year = {2015}
}
@article{Mudigonda2017,
abstract = {Grasping objects with high dimensional controllers such as an anthropomorphic hand using reinforcement learning is a challenging problem. In this work we ex- periment with a 16-D simulated version of a prosthetic hand developed for South- Hampton Hand Assessment Procedure (SHAP). To the best of our knowledge, we provide the first demonstration of learning successful grasping policies for an an- thropomorphic hand from scratch using deep reinforcement learning. We find that our grasping model is robust to sensor noise, variations in object shape, position of the object and physical parameters such as the density of the object. We also test some hypotheses about the utility of touch sensing for grasping objects. We believe that our results and analysis provide useful insights and strong baselines for future research into the very exciting direction of object manipulation with anthropomorphic hands using proprioceptive and sensory feedback. 1},
author = {Mudigonda, Mayur and Agrawal, Pulkit and Deweese, Michael R and Malik, Jitendra},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2017{\_}Investigating Deep Reinforcement Learning for Grasping Objects with an Anthropomorphic Hand{\_}Mudigonda et al.pdf:pdf},
journal = {Deep Reinforcement Learning Symposium, NIPS 2017},
number = {25},
pages = {1--5},
title = {{Investigating Deep Reinforcement Learning for Grasping Objects with an Anthropomorphic Hand}},
year = {2017}
}
@article{Gu2017,
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 offpolicy 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.},
archivePrefix = {arXiv},
arxivId = {1610.00633},
author = {Gu, Shixiang and Holly, Ethan and Lillicrap, Timothy and Levine, Sergey},
doi = {10.1109/ICRA.2017.7989385},
eprint = {1610.00633},
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{\_}Gu et al.pdf:pdf},
isbn = {9781509046331},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
keywords = {Learning and Adaptive Systems},
pages = {3389--3396},
pmid = {26774160},
title = {{Deep Reinforcement Learning for Robotic Manipulation}},
year = {2017}
}
@article{Chen2019a,
abstract = {As a sub-domain of text-to-image synthesis, text-to-face generation has huge potentials in public safety domain. With lack of dataset, there are almost no related research focusing on text-to-face synthesis. In this paper, we propose a fully-trained Generative Adversarial Network (FTGAN) that trains the text encoder and image decoder at the same time for fine-grained text-to-face generation. With a novel fully-trained generative network, FTGAN can synthesize higher-quality images and urge the outputs of the FTGAN are more relevant to the input sentences. In addition, we build a dataset called SCU-Text2face for text-to-face synthesis. Through extensive experiments, the FTGAN shows its superiority in boosting both generated images' quality and similarity to the input descriptions. The proposed FTGAN outperforms the previous state of the art, boosting the best reported Inception Score to 4.63 on the CUB dataset. On SCU-text2face, the face images generated by our proposed FTGAN just based on the input descriptions is of average 59{\%} similarity to the ground-truth, which set a baseline for text-to-face synthesis.},
archivePrefix = {arXiv},
arxivId = {1904.05729},
author = {Chen, Xiang and Qing, Lingbo and He, Xiaohai and Luo, Xiaodong and Xu, Yining},
eprint = {1904.05729},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Chen et al. - 2019 - FTGAN A Fully-trained Generative Adversarial Networks for Text to Face Generation.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}FTGAN A Fully-trained Generative Adversarial Networks for Text to Face Generation{\_}Chen et al.pdf:pdf},
isbn = {2016222050},
month = {apr},
number = {1},
title = {{FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation}},
url = {http://arxiv.org/abs/1904.05729},
year = {2019}
}
@article{Jaderberg2018,
abstract = {Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games. However, the real-world contains multiple agents, each learning and acting independently to cooperate and compete with other agents, and environments reflecting this degree of complexity remain an open challenge. In this work, we demonstrate for the first time that an agent can achieve human-level in a popular 3D multiplayer first-person video game, Quake III Arena Capture the Flag, using only pixels and game points as input. These results were achieved by a novel two-tier optimisation process in which a population of independent RL agents are trained concurrently from thousands of parallel matches with agents playing in teams together and against each other on randomly generated environments. Each agent in the population learns its own internal reward signal to complement the sparse delayed reward from winning, and selects actions using a novel temporally hierarchical representation that enables the agent to reason at multiple timescales. During game-play, these agents display human-like behaviours such as navigating, following, and defending based on a rich learned representation that is shown to encode high-level game knowledge. In an extensive tournament-style evaluation the trained agents exceeded the win-rate of strong human players both as teammates and opponents, and proved far stronger than existing state-of-the-art agents. These results demonstrate a significant jump in the capabilities of artificial agents, bringing us closer to the goal of human-level intelligence.},
archivePrefix = {arXiv},
arxivId = {1807.01281},
author = {Jaderberg, Max and Czarnecki, Wojciech M. and Dunning, Iain and Marris, Luke and Lever, Guy and Castaneda, Antonio Garcia and Beattie, Charles and Rabinowitz, Neil C. and Morcos, Ari S. and Ruderman, Avraham and Sonnerat, Nicolas and Green, Tim and Deason, Louise and Leibo, Joel Z. and Silver, David and Hassabis, Demis and Kavukcuoglu, Koray and Graepel, Thore},
eprint = {1807.01281},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Jaderberg et al. - 2018 - Human-level performance in first-person multiplayer games with population-based deep reinforcement learning(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Human-level performance in first-person multiplayer games with population-based deep reinforcement learning{\_}Jaderberg et al.pdf:pdf},
title = {{Human-level performance in first-person multiplayer games with population-based deep reinforcement learning}},
url = {http://arxiv.org/abs/1807.01281},
year = {2018}
}
@article{Kalashnikov2018,
abstract = {In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execute the desired grasp, our method enables closed-loop vision-based control, whereby the robot continuously updates its grasp strategy based on the most recent observations to optimize long-horizon grasp success. To that end, we introduce QT-Opt, a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters to perform closed-loop, real-world grasping that generalizes to 96{\%} grasp success on unseen objects. Aside from attaining a very high success rate, our method exhibits behaviors that are quite distinct from more standard grasping systems: using only RGB vision-based perception from an over-the-shoulder camera, our method automatically learns regrasping strategies, probes objects to find the most effective grasps, learns to reposition objects and perform other non-prehensile pre-grasp manipulations, and responds dynamically to disturbances and perturbations.},
archivePrefix = {arXiv},
arxivId = {1806.10293},
author = {Kalashnikov, Dmitry and Irpan, Alex and Pastor, Peter and Ibarz, Julian and Herzog, Alexander and Jang, Eric and Quillen, Deirdre and Holly, Ethan and Kalakrishnan, Mrinal and Vanhoucke, Vincent and Levine, Sergey},
eprint = {1806.10293},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}QT-Opt Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation{\_}Kalashnikov et al.pdf:pdf},
keywords = {deep learning,grasping,reinforcement learning},
number = {CoRL},
pages = {1--23},
title = {{QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation}},
url = {http://arxiv.org/abs/1806.10293},
year = {2018}
}
@article{Nanayakkara2013a,
abstract = {Finger-worn interfaces remain a vastly unexplored space for user interfaces, despite the fact that our fingers and hands are naturally used for referencing and interacting with the environment. In this paper we present design guidelines and implementation of a finger-worn I/O device, the EyeRing, which leverages the universal and natural gesture of pointing. We present use cases of EyeRing for both visually impaired and sighted people. We discuss initial reactions from visually impaired users which suggest that EyeRing may indeed offer a more seamless solution for dealing with their immediate surroundings than the solutions they currently use. We also report on a user study that demonstrates how EyeRing reduces effort and disruption to a sighted user. We conclude that this highly promising form factor offers both audiences enhanced, seamless interaction with information related to objects in the environment.},
author = {Nanayakkara, Suranga and Shilkrot, Roy and Yeo, Kian Peen and Maes, Pattie},
doi = {10.1145/2459236.2459240},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2013{\_}EyeRing a finger-worn input device for seamless interactions with our surroundings{\_}Nanayakkara et al.pdf:pdf},
isbn = {9781450319041},
issn = {1133-2654},
journal = {AH '13: Proceedings of the 4th Augmented Human International Conference},
pages = {13--20},
title = {{EyeRing: a finger-worn input device for seamless interactions with our surroundings}},
url = {http://dl.acm.org/citation.cfm?doid=2459236.2459240{\%}5Cnpapers3://publication/doi/10.1145/2459236.2459240},
year = {2013}
}
@article{Lopes2018,
abstract = {We present a mo bile system that enhances mixed r eality experiences and games with force feedback by means of electrical muscle stimulation (EMS). The benefit of our approach is that it adds physical forces while keep ing the users' hands free to interact unencumbered — not only with virtual objects, but also with physical o bjects, such as props and appliances. We demonstrate how this supports three classes of applications along the mixed - reality continuum: (1) enti rely virtual objects, such as furniture with EMS fri c tion when pushed or an EMS - based catapult game. (2) V irtual objects augmented via passive props with EMS - constraints, such as a light control panel made tan g ible by means of a physical cup or a bal ance - the - marble game with an actuated tray. (3) A ugment ed appliances with virtual behaviors, such as a physical t hermo stat dial with EMS - detents or an escape - r oom that repurposes lamps as levers with detents . We present a use r - study in which participants rated the EMS - feedback as significantly more realistic than a no - EMS baseline.},
author = {Lopes, Pedro and You, Sijing and Ion, Alexandra and Baudisch, Patrick},
doi = {10.1145/3173574.3174020},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Lopes et al. - 2018 - Adding Force Feedback to Mixed Reality Experiences and Games using Electrical Muscle Stimulation(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Adding Force Feedback to Mixed Reality Experiences and Games using Electrical Muscle Stimulation{\_}Lopes et al.pdf:pdf},
isbn = {9781450356206},
number = {c},
pages = {1--13},
title = {{Adding Force Feedback to Mixed Reality Experiences and Games using Electrical Muscle Stimulation}},
year = {2018}
}
@article{Zeng2019,
abstract = {We investigate whether a robot arm can learn to pick and throw arbitrary objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. initial pose of object in manipulator) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85{\%} throwing accuracy); and generalizes to new objects and target locations. Videos are available at https://tossingbot.cs.princeton.edu},
archivePrefix = {arXiv},
arxivId = {1903.11239},
author = {Zeng, Andy and Song, Shuran and Lee, Johnny and Rodriguez, Alberto and Funkhouser, Thomas},
eprint = {1903.11239},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Zeng et al. - 2019 - TossingBot Learning to Throw Arbitrary Objects with Residual Physics.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}TossingBot Learning to Throw Arbitrary Objects with Residual Physics{\_}Zeng et al.pdf:pdf},
month = {mar},
title = {{TossingBot: Learning to Throw Arbitrary Objects with Residual Physics}},
url = {http://arxiv.org/abs/1903.11239 https://ai.googleblog.com/2019/03/unifying-physics-and-deep-learning-with.html},
year = {2019}
}
@article{Harwath2017,
abstract = {Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the word 'lighthouse' within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.},
archivePrefix = {arXiv},
arxivId = {1701.07481},
author = {Harwath, David and Glass, James R.},
eprint = {1701.07481},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Harwath, Glass - 2017 - Learning Word-Like Units from Joint Audio-Visual Analysis(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Learning Word-Like Units from Joint Audio-Visual Analysis{\_}Harwath, Glass.pdf:pdf},
title = {{Learning Word-Like Units from Joint Audio-Visual Analysis}},
url = {http://arxiv.org/abs/1701.07481},
year = {2017}
}
@article{Wang2015a,
abstract = {In recent years there have been many successes of using deep representations in reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. In this paper, we present a new neural network architecture for model-free reinforcement learning. Our dueling network represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm. Our results show that this architecture leads to better policy evaluation in the presence of many similar-valued actions. Moreover, the dueling architecture enables our RL agent to outperform the state-of-the-art on the Atari 2600 domain.},
archivePrefix = {arXiv},
arxivId = {1511.06581},
author = {Wang, Ziyu and Schaul, Tom and Hessel, Matteo and van Hasselt, Hado and Lanctot, Marc and de Freitas, Nando},
eprint = {1511.06581},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Wang et al. - 2015 - Dueling Network Architectures for Deep Reinforcement Learning(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}Dueling Network Architectures for Deep Reinforcement Learning{\_}Wang et al.pdf:pdf},
number = {9},
title = {{Dueling Network Architectures for Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1511.06581},
year = {2015}
}
@article{Hilliges2012,
abstract = {Abstract HoloDesk is an interactive system combining an optical see through display and Kinect camera to create the illusion that users are directly interacting with 3D graphics. A virtual image of a 3D scene is rendered through a half silvered mirror and spatially aligned ... $\backslash$n},
author = {Hilliges, Otmar and Kim, David and Izadi, Shahram and Weiss, Malte and Wilson, Andrew},
doi = {10.1145/2207676.2208405},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Hilliges et al. - 2012 - HoloDesk Direct 3D Interactions with a Situated See-Through Display Otmar(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2012{\_}HoloDesk Direct 3D Interactions with a Situated See-Through Display Otmar{\_}Hilliges et al.pdf:pdf},
isbn = {9781450310154},
journal = {Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI '12},
pages = {2421},
title = {{HoloDesk: Direct 3D Interactions with a Situated See-Through Display Otmar}},
url = {http://dl.acm.org/citation.cfm?doid=2207676.2208405},
year = {2012}
}
@article{Yan2019a,
abstract = {Many previous works approach vision-based robotic grasping by training a value network that evaluates grasp proposals. These approaches require an optimization process at run-time to infer the best action from the value network. As a result, the inference time grows exponentially as the dimension of action space increases. We propose an alternative method, by directly training a neural density model to approximate the conditional distribution of successful grasp poses from the input images. We construct a neural network that combines Gaussian mixture and normalizing flows, which is able to represent multi-modal, complex probability distributions. We demonstrate on both simulation and real robot that the proposed actor model achieves similar performance compared to the value network using the Cross-Entropy Method (CEM) for inference, on top-down grasping with a 4 dimensional action space. Our actor model reduces the inference time by 3 times compared to the state-of-the-art CEM method. We believe that actor models will play an important role when scaling up these approaches to higher dimensional action spaces.},
archivePrefix = {arXiv},
arxivId = {1904.07319},
author = {Yan, Mengyuan and Li, Adrian and Kalakrishnan, Mrinal and Pastor, Peter},
eprint = {1904.07319},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Yan et al. - 2019 - Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping{\_}Yan et al.pdf:pdf},
month = {apr},
title = {{Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping}},
url = {http://arxiv.org/abs/1904.07319},
year = {2019}
}
@article{Kajimoto2004,
author = {Kajimoto, Hiroyuki and Kawakami, Naoki and Tachi, Susumu and Inami, Masahiko},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2004{\_}SmartTouch Electric Skin to Touch the{\_}Kajimoto et al.pdf:pdf},
journal = {Image (Rochester, N.Y.)},
number = {February},
pages = {36--43},
title = {{SmartTouch : Electric Skin to Touch the}},
year = {2004}
}
@article{Erat2018,
abstract = {Drones allow exploring dangerous or impassable areas safely from a distant point of view. However, flight control from an egocentric view in narrow or constrained environments can be challenging. Arguably, an exocentric view would afford a better overview and, thus, more intuitive flight control of the drone. Unfortunately, such an exocentric view is unavailable when exploring indoor environments. This paper investigates the potential of drone-augmented human vision, i.e., of exploring the environment and controlling the drone indirectly from an exocentric viewpoint. If used with a see-through display, this approach can simulate X-ray vision to provide a natural view into an otherwise occluded environment. The user's view is synthesized from a three-dimensional reconstruction of the indoor environment using image-based rendering. This user interface is designed to reduce the cognitive load of the drone's flight control. The user can concentrate on the exploration of the inaccessible space, while flight control is largely delegated to the drone's autopilot system. We assess our system with a first experiment showing how drone-augmented human vision supports spatial understanding and improves natural interaction with the drone. IEEE},
author = {Erat, Okan and Isop, Werner Alexander and Kalkofen, Denis and Schmalstieg, Dieter},
doi = {10.1109/TVCG.2018.2794058},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Erat et al. - 2018 - Drone-Augmented human vision Exocentric control for drones exploring hidden areas(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Drone-Augmented human vision Exocentric control for drones exploring hidden areas{\_}Erat et al.pdf:pdf},
issn = {10772626},
journal = {IEEE Transactions on Visualization and Computer Graphics},
keywords = {Drone,Hololens,Mixed reality,Pick-And-place,X-ray},
number = {4},
pages = {1437--1446},
title = {{Drone-Augmented human vision: Exocentric control for drones exploring hidden areas}},
volume = {24},
year = {2018}
}
@article{Polydoros2017a,
abstract = {{\textcopyright} 2017 Springer Science+Business Media DordrechtReinforcement learning is an appealing approach for allowing robots to learn new tasks. Relevant literature reveals a plethora of methods, but at the same time makes clear the lack of implementations for dealing with real life challenges. Current expectations raise the demand for adaptable robots. We argue that, by employing model-based reinforcement learning, the—now limited—adaptability characteristics of robotic systems can be expanded. Also, model-based reinforcement learning exhibits advantages that makes it more applicable to real life use-cases compared to model-free methods. Thus, in this survey, model-based methods that have been applied in robotics are covered. We categorize them based on the derivation of an optimal policy, the definition of the returns function, the type of the transition model and the learned task. Finally, we discuss the applicability of model-based reinforcement learning approaches in new applications, taking into consideration the state of the art in both algorithms and hardware.},
author = {Polydoros, Athanasios S. and Nalpantidis, Lazaros},
doi = {10.1007/s10846-017-0468-y},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Polydoros, Nalpantidis - 2017 - Survey of Model-Based Reinforcement Learning Applications on Robotics.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Survey of Model-Based Reinforcement Learning Applications on Robotics{\_}Polydoros, Nalpantidis.pdf:pdf},
issn = {15730409},
journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
keywords = {Intelligent robotics,Machine learning,Model-based reinforcement learning,Policy search,Reward functions,Robot learning,Transition models},
month = {may},
number = {2},
pages = {153--173},
publisher = {Springer Netherlands},
title = {{Survey of Model-Based Reinforcement Learning: Applications on Robotics}},
url = {http://link.springer.com/10.1007/s10846-017-0468-y},
volume = {86},
year = {2017}
}
@article{Mnih2015a,
abstract = {The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.},
author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg and Petersen, Stig and Beattie, Charles and Sadik, Amir and Antonoglou, Ioannis and King, Helen and Kumaran, Dharshan and Wierstra, Daan and Legg, Shane and Hassabis, Demis},
doi = {10.1038/nature14236},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2015{\_}Human-level control through deep reinforcement learning.{\_}Mnih et al.pdf:pdf},
issn = {1476-4687},
journal = {Nature},
number = {7540},
pages = {529--33},
pmid = {25719670},
publisher = {Nature Publishing Group},
title = {{Human-level control through deep reinforcement learning.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25719670},
volume = {518},
year = {2015}
}
@article{Bhagat2019a,
abstract = {The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics.},
author = {Bhagat, Sarthak and Banerjee, Hritwick and {Ho Tse}, Zion and Ren, Hongliang},
doi = {10.3390/robotics8010004},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Bhagat et al. - 2019 - Deep Reinforcement Learning for Soft, Flexible Robots Brief Review with Impending Challenges.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Deep Reinforcement Learning for Soft, Flexible Robots Brief Review with Impending Challenges{\_}Bhagat et al.pdf:pdf},
journal = {Robotics},
number = {1},
pages = {4},
title = {{Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges}},
volume = {8},
year = {2019}
}
@article{Krizhevsky2012,
abstract = {We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSRVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5{\%} and 17.0{\%} which is considerably better than the previous state of the art. The neural network, which has 60 million paramters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolutional operation. To reduce overfitting in the fully-connected layers, we employed a recently-developed method called 'dropout' that proved to be effective. We also entered a variant of the model in the ILSVRC-2012 competition and achievd a top-5 test error rate of 15.3{\%}, compared to 26.2{\%} achieved by the second-best entry.},
archivePrefix = {arXiv},
arxivId = {1102.0183},
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
doi = {http://dx.doi.org/10.1016/j.protcy.2014.09.007},
eprint = {1102.0183},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2012{\_}ImageNet Classification with Deep Convolutional Neural Networks{\_}Krizhevsky, Sutskever, Hinton.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2012{\_}ImageNet Classification with Deep Convolutional Neural Networks{\_}Krizhevsky, Sutskever, Hinton.pdf:pdf},
isbn = {9781627480031},
issn = {10495258},
journal = {Advances In Neural Information Processing Systems},
pages = {1--9},
pmid = {7491034},
title = {{ImageNet Classification with Deep Convolutional Neural Networks}},
url = {http://papers.nips.cc/paper/4824-imagenet-classification-w{\%}5Cnpapers3://publication/uuid/1ECF396A-CEDA-45CD-9A9F-03344449DA2A},
year = {2012}
}
@article{Ha2018,
abstract = {A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal representations. The world model's extracted features are fed into compact and simple policies trained by evolution, achieving state of the art results in various environments. We also train our agent entirely inside of an environment generated by its own internal world model, and transfer this policy back into the actual environment. Interactive version of paper at https://worldmodels.github.io},
archivePrefix = {arXiv},
arxivId = {1809.01999},
author = {Ha, David and Schmidhuber, J{\"{u}}rgen},
eprint = {1809.01999},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Recurrent World Models Facilitate Policy Evolution{\_}Ha, Schmidhuber.pdf:pdf},
month = {sep},
number = {C},
title = {{Recurrent World Models Facilitate Policy Evolution}},
url = {http://arxiv.org/abs/1809.01999},
year = {2018}
}
@article{Levine2018b,
abstract = {We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.},
archivePrefix = {arXiv},
arxivId = {arXiv:1603.02199v4},
author = {Levine, Sergey and Pastor, Peter and Krizhevsky, Alex and Ibarz, Julian and Quillen, Deirdre},
doi = {10.1177/0278364917710318},
eprint = {arXiv:1603.02199v4},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection{\_}Levine et al(6).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection{\_}Levine et al.{\_}Internation{\_}ijrr.pdf:pdf;:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Levine et al. - 2018 - Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection(7).pdf:pdf;:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Levine et al. - 2018 - Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection(8).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection{\_}Levine et al.pdf:pdf},
issn = {17413176},
journal = {International Journal of Robotics Research},
keywords = {Robotics,deep learning,neural networks},
number = {4-5},
pages = {421--436},
title = {{Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection}},
volume = {37},
year = {2018}
}
@article{OpenAI2018b,
abstract = {We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object's appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM},
archivePrefix = {arXiv},
arxivId = {1808.00177},
author = {OpenAI and Andrychowicz, Marcin and Baker, Bowen and Chociej, Maciek and Jozefowicz, Rafal and McGrew, Bob and Pachocki, Jakub and Petron, Arthur and Plappert, Matthias and Powell, Glenn and Ray, Alex and Schneider, Jonas and Sidor, Szymon and Tobin, Josh and Welinder, Peter and Weng, Lilian and Zaremba, Wojciech},
eprint = {1808.00177},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Learning Dexterous In-Hand Manipulation{\_}OpenAI et al.pdf:pdf;:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/OpenAI et al. - 2018 - Learning Dexterous In-Hand Manipulation.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Learning Dexterous In-Hand Manipulation{\_}OpenAI et al.pdf:pdf},
pages = {1--27},
title = {{Learning Dexterous In-Hand Manipulation}},
url = {http://arxiv.org/abs/1808.00177},
year = {2018}
}
@article{Tuceryan2002a,
abstract = {Augmented reality (AR) is a technology in which a user's view of the real world is enhanced or augmented with additional information generated from a computer model. To have a working AR system, the see-through display system must be calibrated so that the graphics are properly rendered. The optical see-through systems present an additional challenge because, unlike the video see-through systems, we do not have direct access to the image data to be used in various calibration procedures: This paper reports on a calibration method we developed for optical see-through head-mounted displays. We first introduce a method for calibrating monocular optical see-through displays (that is, a display for one eye only) and then extend it to stereo optical see-through displays in which the displays for both eyes are calibrated in a single procedure. The method integrates the measurements for the camera and a six-degrees-of-freedom tracker that is attached to the camera to do the calibration. We have used both an off-the-shelf magnetic tracker as well as a vision-based infrared tracker we have built In the monocular case, the calibration is based on the alignment of image points with a single 3D point in the world coordinate system from various viewpoints. In this method, the user interaction to perform the calibration is extremely easy compared to prior methods, and there is no requirement for keeping the head immobile while performing the calibration. In the stereo calibration case, the user aligns a stereoscopically fused 2D marker, which is perceived in depth, with a single target point in the world whose coordinates are known. As in the monocular case, there is no requirement that the user keep his or her head fixed.},
author = {Tuceryan, Mihran and Genc, Yakup and Navab, Nassir},
doi = {10.1162/105474602317473213},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Tuceryan, Genc, Navab - 2002 - Single-point active alignment method (SPAAM) for optical see-through HMD calibration for augmented rea(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2002{\_}Single-point active alignment method (SPAAM) for optical see-through HMD calibration for augmented reality{\_}Tuceryan, Genc, Navab.pdf:pdf},
issn = {10547460},
journal = {Presence: Teleoperators and Virtual Environments},
number = {3},
pages = {259--276},
title = {{Single-point active alignment method (SPAAM) for optical see-through HMD calibration for augmented reality}},
volume = {11},
year = {2002}
}
@article{Nishi2017,
abstract = {—The device characteristics of semiconductor lasers have been improved with progress in active layer structures. Carrier confinement dimension plays an important role espe-cially in temperature sensitivity as well as slope efficiency. Three-dimensional carrier confinement to nano-scale semiconductor crystal, known as " quantum dots (QDs) " had been predicted to show ultimately superior device performances. Self-assembly formed InAs QDs grown on GaAs had been intensively promoted in order to achieve QD lasers with superior device performances. Now high-density, high-optical quality QDs have been realized through improved molecular beam epitaxy growths and QD lasers with bet-ter temperature characteristics are in the stage of mass-production for a data-com market. Fabry–Perot type, as well as distributed feedback type QD lasers show quite improved laser characteris-tics. Also, the unique device characteristics of QD lasers opened new application fields such as the use for resource searching by utilizing high-temperature operation such as lasing at higher than 200 °C. For silicon-photonics, QD lasers are used as an optical source for athermal operation. In this paper, the evolution of QDs, as well as improved device performances for novel application fields are discussed.},
author = {Nishi, Kenichi and Takemasa, Keizo and Sugawara, Mitsuru and Arakawa, Yasuhiko},
doi = {10.1109/JSTQE.2017.2699787},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Nishi et al. - 2017 - Development of Quantum Dot Lasers for Data-Com and Silicon Photonics Applications(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Development of Quantum Dot Lasers for Data-Com and Silicon Photonics Applications{\_}Nishi et al.pdf:pdf},
issn = {21910359},
journal = {IEEE Journal of Selected Topics in Quantum Electronics},
keywords = {InAs,high-temperature operation,quantum dot lasers,quantum dots,silicon-photonics},
number = {6},
title = {{Development of Quantum Dot Lasers for Data-Com and Silicon Photonics Applications}},
volume = {23},
year = {2017}
}
@article{Zhou2015a,
abstract = {In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels. While this technique was previously proposed as a means for regularizing training, we find that it actually builds a generic localizable deep representation that can be applied to a variety of tasks. Despite the apparent simplicity of global average pooling, we are able to achieve 37.1{\%} top-5 error for object localization on ILSVRC 2014, which is remarkably close to the 34.2{\%} top-5 error achieved by a fully supervised CNN approach. We demonstrate that our network is able to localize the discriminative image regions on a variety of tasks despite not being trained for them},
archivePrefix = {arXiv},
arxivId = {1512.04150},
author = {Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio},
eprint = {1512.04150},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}Learning Deep Features for Discriminative Localization{\_}Zhou et al.pdf:pdf},
pages = {2921--2929},
title = {{Learning Deep Features for Discriminative Localization}},
url = {http://arxiv.org/abs/1512.04150},
year = {2015}
}
@article{Sabour2017,
abstract = {A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule.},
archivePrefix = {arXiv},
arxivId = {1710.09829},
author = {Sabour, Sara and Frosst, Nicholas and Hinton, Geoffrey E},
eprint = {1710.09829},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Sabour, Frosst, Hinton - 2017 - Dynamic Routing Between Capsules(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Dynamic Routing Between Capsules{\_}Sabour, Nov, Hinton.pdf:pdf},
number = {Nips},
title = {{Dynamic Routing Between Capsules}},
url = {http://arxiv.org/abs/1710.09829},
year = {2017}
}
@article{Singh2019,
abstract = {The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory inputs, such as camera images, effectively subsuming both estimation and control into one model. However, real-world applications of reinforcement learning must specify the goal of the task by means of a manually programmed reward function, which in practice requires either designing the very same perception pipeline that end-to-end reinforcement learning promises to avoid, or else instrumenting the environment with additional sensors to determine if the task has been performed successfully. In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task. While requesting labels for every single state would amount to asking the user to manually provide the reward signal, our method requires labels for only a tiny fraction of the states seen during training, making it an efficient and practical approach for learning skills without manually engineered rewards. We evaluate our method on real-world robotic manipulation tasks where the observations consist of images viewed by the robot's camera. In our experiments, our method effectively learns to arrange objects, place books, and drape cloth, directly from images and without any manually specified reward functions, and with only 1-4 hours of interaction with the real world.},
archivePrefix = {arXiv},
arxivId = {1904.07854},
author = {Singh, Avi and Yang, Larry and Hartikainen, Kristian and Finn, Chelsea and Levine, Sergey},
eprint = {1904.07854},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2019{\_}End-to-End Robotic Reinforcement Learning without Reward Engineering{\_}Singh et al.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}End-to-End Robotic Reinforcement Learning without Reward Engineering{\_}Singh et al.pdf:pdf},
month = {apr},
title = {{End-to-End Robotic Reinforcement Learning without Reward Engineering}},
url = {https://sites.google.com/view/reward-learning-rl/home http://arxiv.org/abs/1904.07854 https://github.com/avisingh599/reward-learning-rl http://mrl.snu.ac.kr/research/ProjectScalable/Page.htm},
year = {2019}
}
@article{Chu2018,
abstract = {Neural networks have recently become good at engaging in dialog. However, current approaches are based solely on verbal text, lacking the richness of a real face-to-face conversation. We propose a neural conversation model that aims to read and generate facial gestures alongside with text. This allows our model to adapt its response based on the "mood" of the conversation. In particular, we introduce an RNN encoder-decoder that exploits the movement of facial muscles, as well as the verbal conversation. The decoder consists of two layers, where the lower layer aims at generating the verbal response and coarse facial expressions, while the second layer fills in the subtle gestures, making the generated output more smooth and natural. We train our neural network by having it "watch" 250 movies. We showcase our joint face-text model in generating more natural conversations through automatic metrics and a human study. We demonstrate an example application with a face-to-face chatting avatar.},
author = {Chu, Hang and Li, Daiqing and Fidler, Sanja},
doi = {10.1109/CVPR.2018.00743},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Chu, Li, Fidler - 2018 - A Face-to-Face Neural Conversation Model(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}A Face-to-Face Neural Conversation Model{\_}Chu, Li, Fidler.pdf:pdf},
isbn = {9781538664209},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {7113--7121},
title = {{A Face-to-Face Neural Conversation Model}},
year = {2018}
}
@article{Zhang2019,
abstract = {In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, we seek to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as 1) no extra transfer steps are required during the expensive training of DRL agents in simulation; 2) the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; 3) the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a simple yet effective shift loss that is agnostic to the downstream task, to constrain the consistency between subsequent frames which is important for consistent policy outputs. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in indoor and outdoor robotics experiments.},
author = {Zhang, Jingwei and Tai, Lei and Yun, Peng and Xiong, Yufeng and Liu, Ming and Boedecker, Joschka and Burgard, Wolfram},
doi = {10.1109/LRA.2019.2894216},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Zhang et al. - 2019 - VR-Goggles for Robots Real-to-Sim Domain Adaptation for Visual Control.pdf:pdf},
issn = {23773766},
journal = {IEEE Robotics and Automation Letters},
keywords = {Deep learning in robotics and automation,model learning for control,visual-based navigation},
number = {2},
pages = {1148--1155},
title = {{VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control}},
volume = {4},
year = {2019}
}
@article{Hinton2012,
abstract = {When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random "dropout" gives big improvements on many benchmark tasks and sets new records for speech and object recognition.},
archivePrefix = {arXiv},
arxivId = {1207.0580},
author = {Hinton, Geoffrey E. and Srivastava, Nitish and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan R.},
eprint = {1207.0580},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2012{\_}Improving neural networks by preventing co-adaptation of feature detectors{\_}Hinton et al.pdf:pdf},
isbn = {9781467394673},
pages = {254--256},
title = {{Improving neural networks by preventing co-adaptation of feature detectors}},
url = {http://arxiv.org/abs/1207.0580},
year = {2012}
}
@article{Lee2019a,
abstract = {Many anatomical factors, such as bone geometry and muscle condition, interact to affect human movements. This work aims to build a comprehensive musculoskeletal model and its control system that reproduces realistic human movements driven by muscle contraction dynamics. The variations in the anatomic model generate a spectrum of human movements ranging from typical to highly stylistic movements. To do so, we discuss scalable and reliable simulation of anatomical features, robust control of under-actuated dynamical systems based on deep reinforcement learning, and modeling of pose-dependent joint limits. The key technical contribution is a scalable, two-level imitation learning algorithm that can deal with a comprehensive full-body musculoskeletal model with 346 muscles. We demonstrate the predictive simulation of dynamic motor skills under anatomical conditions including bone deformity, muscle weakness, contracture, and the use of a prosthesis. We also simulate various pathological gaits and predictively visualize how orthopedic surgeries improve post-operative gaits.},
author = {Lee, Seunghwan and Lee, Kyoungmin and Park, Moonseok and Lee, Jehee},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Lee et al. - 2019 - Scalable Muscle-actuated Human Simulation and Control.pdf:pdf;:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Lee et al. - 2019 - Scalable Muscle-Actuated Human Simulation and Control(2).pdf:pdf},
journal = {ACM Transactions on Graphics},
number = {4},
pages = {ACM Transactions on Graphics},
title = {{Scalable Muscle-Actuated Human Simulation and Control}},
url = {http://mrl.snu.ac.kr/research/ProjectScalable/Preprint.pdf{\%}0Ahttps://www.youtube.com/watch?v=a3jfyJ9JVeM{\&}feature=youtu.be http://mrl.snu.ac.kr/research/ProjectScalable/Page.htm http://mrl.snu.ac.kr/research/ProjectScalable/Preprint.pdf},
volume = {38},
year = {2019}
}
@article{James2018b,
abstract = {Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amounts of labelled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power. In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images. We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70{\%} zero-shot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91{\%}, attaining comparable performance to a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99{\%}.},
archivePrefix = {arXiv},
arxivId = {1812.07252},
author = {James, Stephen and Wohlhart, Paul and Kalakrishnan, Mrinal and Kalashnikov, Dmitry and Irpan, Alex and Ibarz, Julian and Levine, Sergey and Hadsell, Raia and Bousmalis, Konstantinos},
eprint = {1812.07252},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Sim-to-Real via Sim-to-Sim Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks{\_}James et al.pdf:pdf},
title = {{Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks}},
url = {http://arxiv.org/abs/1812.07252},
year = {2018}
}
@article{Gu2016,
archivePrefix = {arXiv},
arxivId = {1603.00748},
author = {Gu, Shixiang and Lillicrap, Timothy and Sutskever, Ilya and Levine, Sergey},
doi = {10.3390/robotics2030122},
eprint = {1603.00748},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Gu et al. - 2016 - Continuous deep q-learning with model-based acceleration(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Continuous deep q-learning with model-based acceleration{\_}Gu et al.pdf:pdf},
isbn = {3405062780},
issn = {{\textless}null{\textgreater}},
journal = {International Conference on Machine Learning},
pages = {2829--2838},
pmid = {21487784},
title = {{Continuous deep q-learning with model-based acceleration}},
year = {2016}
}
@article{Harrison2008,
abstract = {We present Scratch Input, an acoustic-based input technique that relies on the unique sound produced when a fingernail is dragged over the surface of a textured material, such as wood, fabric, or wall paint. We employ a simple sensor that can be easily coupled with existing surfaces, such as walls and tables, turning them into large, unpowered and ad hoc finger input surfaces. Our sensor is sufficiently small that it could be incorporated into a mobile device, allowing any suitable surface on which it rests to be appropriated as a gestural input surface. Several example applications were developed to demonstrate possible interactions. We conclude with a study that shows users can perform six Scratch Input gestures at about 90{\%} accuracy with less than five minutes of training and on wide variety of surfaces.},
author = {Harrison, Chris and Hudson, SE Scott E},
doi = {10.1145/1449715.1449747},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Harrison, Hudson - 2008 - Scratch Input Creating Large, Inexpensive, Unpowered and Mobile Finger Input Surfaces(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2008{\_}Scratch Input Creating Large, Inexpensive, Unpowered and Mobile Finger Input Surfaces{\_}Harrison, Hudson.pdf:pdf},
isbn = {978-1-59593-975-3},
journal = {Proceedings of the 21st Annual ACM Symposium on User Interface Software and Technology},
keywords = {acoustic sens-,acoustic sensing,ad hoc interaction,finger input,gestures,ing,mobile devices,surfaces},
pages = {205--208},
title = {{Scratch Input: Creating Large, Inexpensive, Unpowered and Mobile Finger Input Surfaces}},
url = {http://doi.acm.org/10.1145/1449715.1449747{\%}5Cnhttp://dl.acm.org/citation.cfm?id=1449747},
year = {2008}
}
@article{CHIN2012a,
author = {CHIN, Takaaki},
doi = {10.2490/jjrmc.49.31},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/CHIN - 2012 - Myoelectric Prostheses Current Status and Problems to be Solved, a Rehabilitation Strategy for Higher Level Amputation (2).pdf:pdf},
issn = {1881-3526},
journal = {The Japanese Journal of Rehabilitation Medicine},
number = {1},
pages = {31--36},
title = {{Myoelectric Prostheses : Current Status and Problems to be Solved, a Rehabilitation Strategy for Higher Level Amputation Patients, and the Future Outlook}},
volume = {49},
year = {2012}
}
@article{Isola2017b,
abstract = {We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.},
author = {Isola, Phillip and Zhu, Jun Yan and Zhou, Tinghui and Efros, Alexei A.},
doi = {10.1109/CVPR.2017.632},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Image-to-image translation with conditional adversarial networks{\_}Isola et al.pdf:pdf},
isbn = {9781538604571},
journal = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
pages = {5967--5976},
title = {{Image-to-image translation with conditional adversarial networks}},
volume = {2017-Janua},
year = {2017}
}
@article{Lenz2013,
abstract = {We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. First, we need to evaluate a huge number of candidate grasps. In order to make detection fast, as well as robust, we present a two-step cascaded structure with two deep networks, where the top detections from the first are re-evaluated by the second. The first network has fewer features, is faster to run, and can effectively prune out unlikely candidate grasps. The second, with more features, is slower but has to run only on the top few detections. Second, we need to handle multimodal inputs well, for which we present a method to apply structured regularization on the weights based on multimodal group reg-ularization. We demonstrate that our method outperforms the previous state-of-the-art methods in robotic grasp detection, and can be used to successfully execute grasps on a Baxter robot. 1},
author = {Lenz, Ian and Lee, Honglak and Saxena, Ashutosh},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2013{\_}Deep learning for detecting robotic grasps{\_}Lenz, Lee, Saxena.pdf:pdf},
journal = {Robotics: Science adn Systems2},
title = {{Deep Learning for Detecting Robotic Grasps}},
url = {http://www.roboticsproceedings.org/rss09/p12.pdf},
year = {2013}
}
@article{Hashimoto2013,
abstract = {As concern for Japan's aging population and shrinking workforce continues to mount, many researchers are focusing on nursing and healthcare-related robots to help ease the burden on society. In order to successfully integrate robots into the home environment to coexist with family members, three features are considered essential: safe interaction, a compact and lightweight body, and a simple interface. In this paper the authors introduce a robot that possesses these qualities: the human support robot (HSR). The current prototype HSR is designed to support independent living of persons with limited limb mobility. This paper discusses the development of the HSR and the results of user testing conducted in the homes of two persons with disabilities. ? 2013 IEEE.},
author = {Hashimoto, Kunimatsu and Saito, Fuminori and Yamamoto, Takashi and Ikeda, Koichi},
doi = {10.1109/ARSO.2013.6705520},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2013{\_}A field study of the human support robot in the home environment{\_}Hashimoto et al.pdf:pdf},
isbn = {9781479923694},
issn = {21627568},
journal = {Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO},
pages = {143--150},
publisher = {IEEE},
title = {{A field study of the human support robot in the home environment}},
year = {2013}
}
@article{Fei2019,
abstract = {Insects and hummingbirds exhibit extraordinary flight capabilities and can simultaneously master seemingly conflicting goals: stable hovering and aggressive maneuvering, unmatched by small scale man-made vehicles. Flapping Wing Micro Air Vehicles (FWMAVs) hold great promise for closing this performance gap. However, design and control of such systems remain challenging due to various constraints. Here, we present an open source high fidelity dynamic simulation for FWMAVs to serve as a testbed for the design, optimization and flight control of FWMAVs. For simulation validation, we recreated the hummingbird-scale robot developed in our lab in the simulation. System identification was performed to obtain the model parameters. The force generation, open-loop and closed-loop dynamic response between simulated and experimental flights were compared and validated. The unsteady aerodynamics and the highly nonlinear flight dynamics present challenging control problems for conventional and learning control algorithms such as Reinforcement Learning. The interface of the simulation is fully compatible with OpenAI Gym environment. As a benchmark study, we present a linear controller for hovering stabilization and a Deep Reinforcement Learning control policy for goal-directed maneuvering. Finally, we demonstrate direct simulation-to-real transfer of both control policies onto the physical robot, further demonstrating the fidelity of the simulation.},
archivePrefix = {arXiv},
arxivId = {1902.09628},
author = {Fei, Fan and Tu, Zhan and Yang, Yilun and Zhang, Jian and Deng, Xinyan},
eprint = {1902.09628},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Flappy Hummingbird An Open Source Dynamic Simulation of Flapping Wing Robots and Animals{\_}Fei et al.pdf:pdf},
month = {feb},
title = {{Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals}},
url = {http://arxiv.org/abs/1902.09628},
year = {2019}
}
@article{2017,
author = {内田, 祐介 and 山下, 隆義},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}畳み込みニューラルネットワークの研究動向{\_}内田, 山下.pdf:pdf},
journal = {電子情報通信学会 信学技報},
pages = {1--14},
title = {畳み込みニューラルネットワークの研究動向},
year = {2017}
}
@article{kawamura2001,
author = {川村, 次郎},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2001{\_}筋電義手の普及への展望{\_}川村.pdf:pdf},
journal = {日本義肢装具学会誌},
number = {4},
title = {筋電義手の普及への展望},
volume = {17},
year = {2001}
}
@article{Jain2010,
abstract = {Assistive mobile robots that autonomously manipulate objects within everyday settings have the potential to improve the lives of the elderly, injured, and disabled. Within this paper, we present the most recent version of the assistive mobile manipulator EL-E with a focus on the subsystem that enables the robot to retrieve objects from and deliver objects to flat surfaces. Once provided with a 3D location via brief illumination with a laser pointer, the robot autonomously approaches the location and then either grasps the nearest object or places an object. We describe our implementation in detail, while highlighting design principles and themes, including the use of specialized behaviors, task-relevant features, and low-dimensional representations. We also present evaluations of EL-E's performance relative to common forms of variation. We tested EL-E's ability to approach and grasp objects from the 25 object categories that were ranked most important for robotic retrieval by motor-impaired patients from the Emory ALS Center. Although reliability varied, EL-E succeeded at least once with objects from 21 out of 25 of these categories. EL-E also approached and grasped a cordless telephone on 12 different surfaces including floors, tables, and counter tops with 100{\%} success. The same test using a vitamin pill (ca. 15 mm × 5 mm × 5 mm) resulted in 58{\%} success. {\textcopyright} 2009 Springer Science+Business Media, LLC.},
author = {Jain, Advait and Kemp, Charles C.},
doi = {10.1007/s10514-009-9148-5},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2010{\_}EL-E An assistive mobile manipulator that autonomously fetches objects from flat surfaces{\_}Jain, Kemp.pdf:pdf},
issn = {09295593},
journal = {Autonomous Robots},
keywords = {Assistive robotics,Mobile manipulation},
number = {1},
pages = {45--64},
title = {{EL-E: An assistive mobile manipulator that autonomously fetches objects from flat surfaces}},
volume = {28},
year = {2010}
}
@article{Wang2007,
abstract = {The STanford Artificial Intelligence Robot (STAIR) project is a long-term group effort aimed at producing a viable home and office assistant robot. As a small concrete step towards this goal, we showed a demonstration video at the 2007 AAAI Mobile Robot Exhibition of the STAIR 1 robot responding to a verbal command to fetch an item. Carrying out this task involved the integration of multiple components, including spoken dialog, navigation, computer visual object detection, and robotic grasping. This paper describes the hardware and software integration frameworks used to facilitate the devel- opment of these components and to bring them together for the demonstration.},
author = {Wang, Endong and Zhang, Qing and Shen, Bo and Zhang, Guangyong and Lu, Xiaowei and Wu, Qing and Wang, Yajuan and Wang, Endong and Zhang, Qing and Shen, Bo and Zhang, Guangyong and Lu, Xiaowei and Wu, Qing and Wang, Yajuan},
doi = {10.1007/978-3-319-06486-4_2},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Wang et al. - 2007 - STAIR Hardware and Software Architecture.pdf:pdf},
journal = {Association for the Advancement of Artificial Intelligence},
pages = {31--37},
title = {{STAIR: Hardware and Software Architecture}},
year = {2007}
}
@article{Stuckler2016,
abstract = {Cognitive service robots that shall assist persons in need in performing their activities of daily living have recently received much attention in robotics research. Such robots require a vast set of control and perception capabilities to provide useful assistance through mobile manipulation and human-robot interaction. In this article, we present hardware design, perception, and control methods for our cognitive service robot Cosero. We complement autonomous capabilities with handheld teleoperation interfaces on three levels of autonomy. The robot demonstrated various advanced skills, including the use of tools. With our robot we participated in the annual international RoboCup@Home competitions, winning them three times in a row.},
author = {St{\"{u}}ckler, J{\"{o}}rg and Schwarz, Max and Behnke, Sven},
doi = {10.3389/frobt.2016.00058},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Mobile Manipulation, Tool Use, and Intuitive Interaction for Cognitive Service Robot Cosero{\_}St{\"{u}}ckler, Schwarz, Behnke.pdf:pdf},
journal = {Frontiers in Robotics and AI},
keywords = {cognitive service robots,cognitive service robots, mobile manipulation, obj,human,mobile manipulation,object perception,robot interaction,shared},
number = {November},
title = {{Mobile Manipulation, Tool Use, and Intuitive Interaction for Cognitive Service Robot Cosero}},
volume = {3},
year = {2016}
}
@article{Chitta2010,
abstract = {Computing a motion that enables a mobile manip- ulator to open a door is challenging because it requires tight coordination between the motions of the arm and the base. Hard-coding the motion, on the other hand, is infeasible since doors vary widely in their sizes and types, some doors are opened by pulling and others by pushing, and indoor spaces often contain obstacles that limit the freedom of the mobile manipulator and the degree to which the doors open up. In this paper, we show how to overcome the high-dimensionality of the planning problem by identifying a graph-based representation that is small enough for efficient planning yet rich enough to contain feasible motions that open doors. The use of graph search-based motion planning enables us to handle consistently the wide variance of conditions under which doors need to be open. We demonstrate our approach on the PR2 robot - a mobile manipulator with an omnidirectional base and a 7 degree of freedom arm. The robot was successful in opening a variety of doors both by pulling and pushing.},
author = {Chitta, Sachin and Cohen, Benjamin and Likhachev, Maxim},
doi = {10.1109/ROBOT.2010.5509475},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2010{\_}Planning for autonomous door opening with a mobile manipulator{\_}Chitta, Cohen, Likhachev.pdf:pdf},
isbn = {9781424450381},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
pages = {1799--1806},
publisher = {IEEE},
title = {{Planning for autonomous door opening with a mobile manipulator}},
year = {2010}
}
@article{Pages2016,
abstract = {— This paper describes TIAGo, the mobile manip-ulator created by PAL Robotics, which was conceived since its very begining as a modular robot. TIAGo inherits several modular components from its eldest brothers REEM, the service robot, and REEM-C, the biped humanoid robot, and incorporates new modular parts that can be used to build up other robots. The modularity techniques used to create TIAGo are introduced in this paper.},
author = {Pages, Jordi and Marchionni, Luca and Ferro, Francesco},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}TIAGo the modular robot that adapts to different research needs{\_}Pages, Marchionni, Ferro.pdf:pdf},
pages = {3--6},
title = {{TIAGo: the modular robot that adapts to different research needs}},
url = {https://clawar.org/wp-content/uploads/2016/10/P2.pdf},
year = {2016}
}
@article{MeloneeWise2016,
abstract = {Since the introduction and widespread adop-tion of the Robot Operating System (ROS), the mobile manipulation and mobile service robot communities have seen great advances in robot capabilities. However, the lack of af-fordable and commercially available fully inte-grated standard platforms remains a major bar-rier to further and faster advances. The Fetch mobile manipulator is designed to be the afford-able standard platform for the next generation of mobile manipulator applications. Fetch's lit-tle brother, Freight, is designed to be the afford-able standard platform for mobile service robot applications. This paper highlights the design decisions and trade-offs made in achieving the low cost of the platforms while continuing to provide the required capabilities for such appli-cations.},
author = {{Melonee Wise} and Ferguson, Michael and King, Derek and Diehr, Eric and Dymesich, David},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Fetch {\&} Freight Standard Platforms for Service Robot Applications{\_}Melonee Wise et al.pdf:pdf},
journal = {Workshop on Autonomous Mobile Service Robots, held at the 2016 International Joint Conference on Artificial Intelligence},
pages = {2--7},
title = {{Fetch {\&} Freight: Standard Platforms for Service Robot Applications}},
url = {http://fetchrobotics.com/research/},
year = {2016}
}
@article{Iriondo2019,
abstract = {Programming robots to perform complex tasks is a very expensive job. Traditional path planning and control are able to generate point to point collision free trajectories, but when the tasks to be performed are complex, traditional planning and control become complex tasks. This study focused on robotic operations in logistics, specifically, on picking objects in unstructured areas using a mobile manipulator configuration. The mobile manipulator has to be able to place its base in a correct place so the arm is able to plan a trajectory up to an object in a table. A deep reinforcement learning (DRL) approach was selected to solve this type of complex control tasks. Using the arm planner's feedback, a controller for the robot base is learned, which guides the platform to such a place where the arm is able to plan a trajectory up to the object. In addition the performance of two DRL algorithms ((Deep Deterministic Policy Gradient (DDPG)) and (Proximal Policy Optimisation (PPO)) is compared within the context of a concrete robotic task.},
author = {Iriondo, Ander and Lazkano, Elena and Susperregi, Loreto and Urain, Julen and Fernandez, Ane and Molina, Jorge},
doi = {10.3390/app9020348},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Pick and Place Operations in Logistics Using a Mobile Manipulator Controlled with Deep Reinforcement Learning{\_}Iriondo et al.pdf:pdf},
journal = {Applied Sciences},
keywords = {deep reinforcement learning,mobile manipulation,robot learning},
number = {2},
pages = {348},
title = {{Pick and Place Operations in Logistics Using a Mobile Manipulator Controlled with Deep Reinforcement Learning}},
volume = {9},
year = {2019}
}
@article{Li2018b,
abstract = {Abstract—It is important for humanoid-like mobile robots to learn the complex motion sequences in human-robot environment such that the robots can adapt such motions. This paper describes a reinforcement learning (RL) strategy for manipulation and grasping of a mobile manipulator, which reduces the complexity of the visual feedback and handle varying manipulation dynamics and uncertain external perturbations. Two hierarchies plannings have been considered in the proposed strategy, high-level online redundancy resolution based on the neural-dynamic optimization algorithm in operational space, and low-level reinforcement learning in joint space. At this level, the dynamic movement primitives (DMPs) have been considered to model and learn the joint trajectories, and then the reinforcement learning is em- ployed to learn the trajectories with uncertainties. Experimental results on the developed humanoid-like mobile robot demonstrate that the presented approach can suppress the uncertain external perturbations.},
author = {Li, Zhijun and Zhao, Ting and Chen, Fei and Hu, Yingbai and Su, Chun Yi and Fukuda, Toshio},
doi = {10.1109/TMECH.2017.2717461},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Reinforcement Learning of Manipulation and Grasping Using Dynamical Movement Primitives for a Humanoidlike Mobile Manipulator{\_}Li et.pdf:pdf},
issn = {10834435},
journal = {IEEE/ASME Transactions on Mechatronics},
keywords = {Dynamic movement primitive (DMP),mobile manipulation,redundancy resolution,reinforcement learning (RL)},
number = {1},
pages = {121--131},
publisher = {IEEE},
title = {{Reinforcement Learning of Manipulation and Grasping Using Dynamical Movement Primitives for a Humanoidlike Mobile Manipulator}},
volume = {23},
year = {2018}
}
@article{Tamei2011,
abstract = {This study aims at robotic clothing assistance as it is yet an open field for robotics despite it is one of the basic and important assistance activities in daily life of elderly as well as disabled people. The clothing assistance is a challenging problem since robots must interact with non-rigid clothes generally represented in a high-dimensional space, and with the assisted person whose posture can vary during the assistance. Thus, the robot is required to manage two difficulties to perform the task of the clothing assistance: 1) handling of non-rigid materials and 2) adaptation of the assisting movements to the assisted person's posture. To overcome these difficulties, we propose to use reinforcement learning with the cloth's state which is low-dimensionally represented in topology coordinates, and with the reward defined in the low-dimensional coordinates. With our developed experimental system, for T-shirt clothing assistance, including an anthropomorphic dual-arm robot and a soft mannequin, we demonstrate the robot quickly learns a suitable arm motion for putting the mannequin's head into a T-shirt.},
author = {Tamei, Tomoya and Matsubara, Takamitsu and Rai, Akshara and Shibata, Tomohiro},
doi = {10.1109/Humanoids.2011.6100915},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2011{\_}Reinforcement learning of clothing assistance with a dual-arm robot{\_}Tamei et al.pdf:pdf},
isbn = {9781612848679},
issn = {21640572},
journal = {IEEE-RAS International Conference on Humanoid Robots},
pages = {733--738},
publisher = {IEEE},
title = {{Reinforcement learning of clothing assistance with a dual-arm robot}},
year = {2011}
}
@article{Heinig1997,
abstract = {Cultured female white sturgeon (Acipenser transmontanus ) fail to reach sexual maturity be 8 years of age even though cultured males reach sexual maturity by 3-4 years of age. Sexual development in the cultured females is arrested at the previtellogenic stage with insufficient follicular development to initiate estrogen synthesis. Exogenous estradiol treatment will induce vitellogenin synthesis in these fish, but even when the plasma concentrations of vitellogenin are elevated, the follicle is not capable of incorporating vitellogenin. Administration of synthetic gonadotropin releasing hormone (LHRHa) alone or in combination with estradiol did not induce uptake of vitellogenin by the follicle. Likewise, administration of carp pituitary extract in combination with estradiol did not influence vitellogenin uptake. The combined treatment of estradiol and LHRHa resulted in significantly higher circulating concentrations of estrogens than the administration of estradiol alone, suggesting that the combined treatment may be the result of induced estrogen synthesis.},
author = {Heinig, M. Jane},
doi = {10.1177/089033449701300102},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2010{\_}Medical and Health-Care Robotics{\_}Heinig.pdf:pdf},
issn = {08903344},
journal = {Journal of Human Lactation},
number = {1},
pages = {3--4},
title = {{Medical and Health-Care Robotics}},
volume = {13},
year = {2010}
}
@article{Yamamoto2018a,
abstract = {There has been an increasing interest in mobile manipulators that is capable of performing physical work in living spaces worldwide, corresponding to population aging with declining birth rates with the expectation of improving quality of life (QOL). Research and development is a must in intelligent sensing and software which enable advanced recognition, judgment, and motion to realize household work by robots. In order to accelerate this research, we have developed a compact and safe research platform, Human Support Robot (HSR), which can be operated in an actual home environment. We assume that overall R{\&}D will accelerate by using a common robot platform among many researchers since that enables them to Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored.},
author = {Yamamoto, Takashi and Nishino, Tamaki and Kajima, Hideki and Ohta, Mitsunori and Ikeda, Koichi},
doi = {10.1145/3214907.3233972},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Human support robot (HSR){\_}Yamamoto et al.pdf:pdf},
isbn = {9781450358101},
pages = {1--2},
title = {{Human support robot (HSR)}},
url = {https://doi.org/10.1145/3214907.3233972},
year = {2018}
}
@article{Lu2017,
abstract = {—In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for scalable image search. Unlike most existing binary codes learning methods which usually seek a single linear projection to map each sample into a binary feature vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the developed deep network: 1) the loss between the compact real-valued code and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) and multi-label supervised DH (MSDH) by including a discriminative term into the objective function of DH which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes with the single-label and multi-label settings, respectively. Extensive experimental results on eight widely used image search datasets show

license_type: permissive

branch_name: refs/heads/master

extension: bib