text:
that our proposed methods achieve very competitive results with the state-of-the-arts.},
author = {Lu, Jiwen and Liong, Venice Erin and Zhou, Jie},
doi = {10.1109/TIP.2017.2678163},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Deep Hashing for Scalable Image Search{\_}Lu, Liong, Zhou.pdf:pdf},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Scalable image search,deep learning,fast similarity search,hashing,multi-label learning},
number = {5},
pages = {2352--2367},
title = {{Deep Hashing for Scalable Image Search}},
volume = {26},
year = {2017}
}
@article{Tahara2013,
author = {Tahara, Kenji},
doi = {10.7210/jrsj.31.364},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2013{\_}Object Manipulation based on the Dynamic Stability{\_}Tahara.pdf:pdf},
issn = {0289-1824},
journal = {Journal of the Robotics Society of Japan},
number = {4},
pages = {364--369},
title = {{Object Manipulation based on the Dynamic Stability}},
volume = {31},
year = {2013}
}
@article{1393,
author = {星川, 英 and 迫田, 辰太郎 and 山野井, 佑介 and 加藤, 龍 and 森下, 壮一郎 and 中村, 達弘 and 關, 達也 and 姜, 銀来 and 横井, 浩史},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}基本把持機能を有する簡易型筋電義手の開発と評価{\_}星川 et al.pdf:pdf},
journal = {知能と情報(日本知能情報ファジィ学会誌)},
number = {6},
pages = {885},
title = {基本把持機能を有する簡易型筋電義手の開発と評価},
volume = {27},
year = {2015}
}
@article{Tani2019,
author = {Tani, Naoyuki and Jiang, Yinlai and Yokoi, Hiroshi},
doi = {https://doi.org/10.7210/jrsj.37.168},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}握力把握・精密把握における安定把持のための筋電義手用関節屈伸機構の開発{\_}Tani, Jiang, Yokoi.pdf:pdf},
journal = {日本ロボット学会誌},
keywords = {grasp stability,myoelectric prosthetic hand,power grasp,precision grasp,sti ff ness},
number = {2},
pages = {168--178},
title = {握力把握・精密把握における安定把持のための筋電義手用関節屈伸機構の開発},
volume = {37},
year = {2019}
}
@article{Eysenbach2018,
abstract = {Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose DIAYN ('Diversity is All You Need'), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy. On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping. In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward. We show how pretrained skills can provide a good parameter initialization for downstream tasks, and can be composed hierarchically to solve complex, sparse reward tasks. Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning.},
archivePrefix = {arXiv},
arxivId = {1802.06070},
author = {Eysenbach, Benjamin and Gupta, Abhishek and Ibarz, Julian and Levine, Sergey},
eprint = {1802.06070},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Diversity is All You Need Learning Skills without a Reward Function{\_}Eysenbach et al.pdf:pdf},
month = {feb},
title = {{Diversity is All You Need: Learning Skills without a Reward Function}},
url = {http://arxiv.org/abs/1802.06070},
year = {2018}
}
@article{Sather2019,
abstract = {Autonomous harvesting may provide a viable solution to mounting labor pressures in the United States's strawberry industry. However, due to bottlenecks in machine perception and economic viability, a profitable and commercially adopted strawberry harvesting system remains elusive. In this research, we explore the feasibility of using deep reinforcement learning to overcome these bottlenecks and develop a practical algorithm to address the sub-objective of viewpoint optimization, or the development of a control policy to direct a camera to favorable vantage points for autonomous harvesting. We evaluate the algorithm's performance in a custom, open-source simulated environment and observe affirmative results. Our trained agent yields 8.7 times higher returns than random actions and 8.8 percent faster exploration than our best baseline policy, which uses visual servoing. Visual investigation shows the agent is able fixate on favorable viewpoints, despite having no explicit means to propagate information through time. Overall, we conclude that deep reinforcement learning is a promising area of research to advance the state of the art in autonomous strawberry harvesting.},
archivePrefix = {arXiv},
arxivId = {1903.02074},
author = {Sather, Jonathon},
eprint = {1903.02074},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement Learning{\_}Sather.pdf:pdf},
month = {mar},
title = {{Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1903.02074},
year = {2019}
}
@article{Botvinick2019,
abstract = {Deep reinforcement learning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go to no-limit poker. This progress has drawn the attention of cognitive scientists interested in understanding human learning. However, the concern has been raised that deep RL may be too sample-inefficient – that is, it may simply be too slow – to provide a plausible model of how humans learn. In the present review, we counter this critique by describing recently developed techniques that allow deep RL to operate more nimbly, solving problems much more quickly than previous methods. Although these techniques were developed in an AI context, we propose that they may have rich implications for psychology and neuroscience. A key insight, arising from these AI methods, concerns the fundamental connection between fast RL and slower, more incremental forms of learning.},
author = {Botvinick, Mathew and Ritter, Sam and Wang, Jane X. and Kurth-Nelson, Zeb and Blundell, Charles and Hassabis, Demis},
doi = {10.1016/j.tics.2019.02.006},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Reinforcement Learning, Fast and Slow{\_}Botvinick et al.pdf:pdf},
issn = {1879307X},
journal = {Trends in Cognitive Sciences},
month = {apr},
number = {0},
pmid = {31003893},
publisher = {Elsevier},
title = {{Reinforcement Learning, Fast and Slow}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/31003893},
volume = {0},
year = {2019}
}
@article{Lecarpentier2019,
abstract = {This work tackles the problem of robust zero-shot planning in non-stationary stochastic environments. We study Markov Decision Processes (MDPs) evolving over time and consider Model-Based Reinforcement Learning algorithms in this setting. We make two hypotheses: 1) the environment evolves continuously and its evolution rate is bounded, 2) a current model is known at each decision epoch but not its evolution. Our contribution can be presented in four points. First, we define this specific class of MDPs that we call Non-Stationary MDPs (NSMDPs). We introduce the notion of regular evolution by making an hypothesis of Lipschitz-Continuity on the transition and reward functions w.r.t. time. Secondly, we consider a planning agent using the current model of the environment, but unaware of its future evolution. This leads us to consider a worst-case method where the environment is seen as an adversarial agent. Third, following this approach, we propose the Risk-Averse Tree-Search (RATS) algorithm. This is a zero-shot Model-Based method similar to Minimax search. Finally, we illustrate the benefits brought by RATS empirically and compare its performance with reference Model-Based algorithms.},
archivePrefix = {arXiv},
arxivId = {1904.10090},
author = {Lecarpentier, Erwan and Rachelson, Emmanuel},
eprint = {1904.10090},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning{\_}Lecarpentier, Rachelson.pdf:pdf},
month = {apr},
title = {{Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning}},
url = {http://arxiv.org/abs/1904.10090},
year = {2019}
}
@techreport{Silver2018,
abstract = {We present Residual Policy Learning (RPL): a simple method for improving nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in complex robotic manipulation tasks where good but imperfect controllers are available. In these tasks, reinforcement learning from scratch remains data-inefficient or intractable, but learning a residual on top of the initial controller can yield substantial improvements. We study RPL in six challenging MuJoCo tasks involving partial observability, sensor noise, model misspecification, and controller miscalibration. For initial controllers, we consider both hand-designed policies and model-predictive controllers with known or learned transition models. By combining learning with control algorithms, RPL can perform long-horizon, sparse-reward tasks for which reinforcement learning alone fails. Moreover, we find that RPL consistently and substantially improves on the initial controllers. We argue that RPL is a promising approach for combining the complementary strengths of deep reinforcement learning and robotic control, pushing the boundaries of what either can achieve independently. Video and code at https://k-r-allen.github.io/residual-policy-learning/.},
archivePrefix = {arXiv},
arxivId = {1812.06298},
author = {Silver, Tom and Allen, Kelsey and Tenenbaum, Josh and Kaelbling, Leslie},
eprint = {1812.06298},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Residual Policy Learning{\_}Silver et al.pdf:pdf},
isbn = {1812.06298v2},
title = {{Residual Policy Learning}},
url = {http://arxiv.org/abs/1812.06298},
year = {2018}
}
@techreport{Johannink2018,
abstract = {Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. Hence, applying control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment. Reinforcement learning (RL) methods have been demonstrated to be capable of learning continuous robot controllers from interactions with the environment, even for problems that include friction and contacts. In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL. The final control policy is a superposition of both control signals. We demonstrate our approach by training an agent to successfully perform a real-world block assembly task involving contacts and unstable objects.},
archivePrefix = {arXiv},
arxivId = {1812.03201},
author = {Johannink, Tobias and Bahl, Shikhar and Nair, Ashvin and Luo, Jianlan and Kumar, Avinash and Loskyll, Matthias and Ojea, Juan Aparicio and Solowjow, Eugen and Levine, Sergey},
eprint = {1812.03201},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Residual Reinforcement Learning for Robot Control{\_}Johannink et al.pdf:pdf},
title = {{Residual Reinforcement Learning for Robot Control}},
url = {http://arxiv.org/abs/1812.03201},
year = {2018}
}
@techreport{Ho2016,
abstract = {Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.},
archivePrefix = {arXiv},
arxivId = {1606.03476},
author = {Ho, Jonathan and Ermon, Stefano},
eprint = {1606.03476},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Generative Adversarial Imitation Learning{\_}Ho, Ermon.pdf:pdf},
title = {{Generative Adversarial Imitation Learning}},
url = {http://arxiv.org/abs/1606.03476},
year = {2016}
}
@article{Ha2018b,
abstract = {We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment. An interactive version of this paper is available at https://worldmodels.github.io/},
archivePrefix = {arXiv},
arxivId = {1803.10122},
author = {Ha, David and Schmidhuber, J{\"{u}}rgen},
doi = {10.5281/zenodo.1207631},
eprint = {1803.10122},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Ha, Schmidhuber - 2018 - World Models.pdf:pdf},
month = {mar},
title = {{World Models}},
url = {http://arxiv.org/abs/1803.10122{\%}0Ahttp://dx.doi.org/10.5281/zenodo.1207631 https://worldmodels.github.io},
year = {2018}
}
@article{Oh2018,
abstract = {This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions. This algorithm is designed to verify our hypothesis that exploiting past good experiences can indirectly drive deep exploration. Our empirical results show that SIL significantly improves advantage actor-critic (A2C) on several hard exploration Atari games and is competitive to the state-of-the-art count-based exploration methods. We also show that SIL improves proximal policy optimization (PPO) on MuJoCo tasks.},
archivePrefix = {arXiv},
arxivId = {1806.05635},
author = {Oh, Junhyuk and Guo, Yijie and Singh, Satinder and Lee, Honglak},
eprint = {1806.05635},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Self-Imitation Learning{\_}Oh et al.pdf:pdf},
month = {jun},
title = {{Self-Imitation Learning}},
url = {http://arxiv.org/abs/1806.05635},
year = {2018}
}
@article{Hester2017,
abstract = {Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator's actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD's performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.},
archivePrefix = {arXiv},
arxivId = {1704.03732},
author = {Hester, Todd and Vecerik, Matej and Pietquin, Olivier and Lanctot, Marc and Schaul, Tom and Piot, Bilal and Horgan, Dan and Quan, John and Sendonaris, Andrew and Dulac-Arnold, Gabriel and Osband, Ian and Agapiou, John and Leibo, Joel Z. and Gruslys, Audrunas},
eprint = {1704.03732},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2017{\_}Deep Q-learning from Demonstrations{\_}Hester et al.pdf:pdf},
month = {apr},
title = {{Deep Q-learning from Demonstrations}},
url = {http://arxiv.org/abs/1704.03732},
year = {2017}
}
@article{Lee2019,
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},
journal = {ACM Transactions on Graphics},
title = {{Scalable Muscle-actuated Human Simulation and Control}},
url = {http://mrl.snu.ac.kr/research/ProjectScalable/Page.htm http://mrl.snu.ac.kr/research/ProjectScalable/Preprint.pdf},
year = {2019}
}
@article{Singh2019a,
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},
month = {apr},
title = {{End-to-End Robotic Reinforcement Learning without Reward Engineering}},
url = {http://arxiv.org/abs/1904.07854},
year = {2019}
}
@article{Buslaev2018,
abstract = {Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve output labels. In computer vision domain, image augmentations have become a common implicit regularization technique to combat overfitting in deep convolutional neural networks and are ubiquitously used to improve performance. While most deep learning frameworks implement basic image transformations, the list is typically limited to some variations and combinations of flipping, rotating, scaling, and cropping. Moreover, the image processing speed varies in existing tools for image augmentation. We present Albumentations, a fast and flexible library for image augmentations with many various image transform operations available, that is also an easy-to-use wrapper around other augmentation libraries. We provide examples of image augmentations for different computer vision tasks and show that Albumentations is faster than other commonly used image augmentation tools on the most of commonly used image transformations. The source code for Albumentations is made publicly available online at https://github.com/albu/albumentations},
archivePrefix = {arXiv},
arxivId = {1809.06839},
author = {Buslaev, Alexander and Parinov, Alex and Khvedchenya, Eugene and Iglovikov, Vladimir I. and Kalinin, Alexandr A.},
eprint = {1809.06839},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Albumentations fast and flexible image augmentations{\_}Buslaev et al.pdf:pdf},
month = {sep},
title = {{Albumentations: fast and flexible image augmentations}},
url = {http://arxiv.org/abs/1809.06839},
year = {2018}
}
@article{Wulfmier2018a,
abstract = {In order to enable more widespread application of robots, we are required to reduce the human effort for the introduction of existing robotic platforms to new environments and tasks. In this thesis, we identify three complementary strategies to address this challenge, via the use of imitation learning, domain adaptation, and transfer learning based on simulations. The overall work strives to reduce the effort of generating training data by employing inexpensively obtainable labels and by transferring information between different domains with deviating underlying properties.
Imitation learning enables a straightforward way for untrained personnel to teach robots to perform tasks by providing demonstrations, which represent a comparably inexpensive source of supervision. We develop a scalable approach to identify the preferences underlying demonstration data via the framework of inverse reinforcement learning. The method enables integration of the extracted preferences as cost maps into existing motion planning systems. We further incorporate prior domain knowledge and demonstrate that the approach outperforms the baselines including manually crafted cost functions.
In addition to employing low-cost labels from demonstration, we investigate the adaptation of models to domains without available supervisory information. Specifically, the challenge of appearance changes in outdoor robotics such as illumination and weather shifts is addressed using an adversarial domain adaptation approach. A principal advantage of the method over prior work is the straightforwardness of adapting arbitrary, state-of-the-art neural network architectures. Finally, we demonstrate performance benefits of the method for semantic segmentation of drivable terrain.
Our last contribution focuses on simulation to real world transfer learning, where the characteristic differences are not only regarding the visual appearance but the underlying system dynamics. Our work aims at parallel training in both systems and mutual guidance via auxiliary alignment rewards to accelerate training for real world systems. The approach is shown to outperform various baselines as well as a unilateral alignment variant.},
archivePrefix = {arXiv},
arxivId = {1904.07346},
author = {Wulfmier, Markus},
doi = {10.1007/s13218-019-00587-0},
eprint = {1904.07346},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Efficient Supervision for Robot Learning via Imitation, Simulation, and Adaptation{\_}Wulfmier.pdf:pdf},
month = {apr},
title = {{Efficient Supervision for Robot Learning via Imitation, Simulation, and Adaptation}},
url = {https://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc},
year = {2018}
}
@article{Kirillov2019,
abstract = {The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation.},
archivePrefix = {arXiv},
arxivId = {1901.02446},
author = {Kirillov, Alexander and Girshick, Ross and He, Kaiming and Doll{\'{a}}r, Piotr},
eprint = {1901.02446},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Panoptic Feature Pyramid Networks{\_}Kirillov et al.pdf:pdf},
month = {jan},
title = {{Panoptic Feature Pyramid Networks}},
url = {http://arxiv.org/abs/1901.02446},
year = {2019}
}
@techreport{Bousmalis2016,
abstract = {The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach circumventing this cost is training models on synthetic data where annotations are provided automatically. Despite their appeal, such models often fail to generalize from synthetic to real images, necessitating domain adaptation algorithms to manipulate these models before they can be successfully applied. Existing approaches focus either on mapping representations from one domain to the other, or on learning to extract features that are invariant to the domain from which they were extracted. However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain. We suggest that explicitly modeling what is unique to each domain can improve a model's ability to extract domain-invariant features. Inspired by work on private-shared component analysis, we explicitly learn to extract image representations that are partitioned into two subspaces: one component which is private to each domain and one which is shared across domains. Our model is trained not only to perform the task we care about in the source domain, but also to use the partitioned representation to reconstruct the images from both domains. Our novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process.},
archivePrefix = {arXiv},
arxivId = {1608.06019},
author = {Bousmalis, Konstantinos and Trigeorgis, George and Silberman, Nathan and Krishnan, Dilip and Erhan, Dumitru},
eprint = {1608.06019},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Domain Separation Networks{\_}Bousmalis et al.pdf:pdf},
title = {{Domain Separation Networks}},
url = {http://arxiv.org/abs/1608.06019},
year = {2016}
}
@article{Chang2015,
abstract = {We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans.},
archivePrefix = {arXiv},
arxivId = {1512.03012},
author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
eprint = {1512.03012},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}ShapeNet An Information-Rich 3D Model Repository{\_}Chang et al.pdf:pdf},
month = {dec},
title = {{ShapeNet: An Information-Rich 3D Model Repository}},
url = {http://arxiv.org/abs/1512.03012},
year = {2015}
}
@article{Matas2018,
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},
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{Zhang2018b,
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},
journal = {IEEE Robotics and Automation Letters},
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 = {2018}
}
@inproceedings{Pinto2016,
abstract = {Current learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial-and-error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over-fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18-way binary classification over image patches. We also present a multi-stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large-scale datasets (and multi-stage training) for the task of grasping. We also compare to several baselines and show state-of-the-art performance on generalization to unseen objects for grasping.},
author = {Pinto, Lerrel and Gupta, Abhinav},
booktitle = {Proceedings - IEEE International Conference on Robotics and Automation},
doi = {10.1109/ICRA.2016.7487517},
isbn = {9781467380263},
issn = {10504729},
month = {may},
pages = {3406--3413},
publisher = {IEEE},
title = {{Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours}},
url = {http://ieeexplore.ieee.org/document/7487517/},
volume = {2016-June},
year = {2016}
}
@article{Lenz2015,
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 regularization. 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 two different robotic platforms.},
author = {Lenz, Ian and Lee, Honglak and Saxena, Ashutosh},
doi = {10.1177/0278364914549607},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2013{\_}Deep learning for detecting robotic grasps{\_}Lenz, Lee, Saxena.pdf:pdf},
issn = {17413176},
journal = {International Journal of Robotics Research},
keywords = {3D feature learning,Baxter,PR2,RGB-D multi-modal data,Robotic grasping,deep learning},
month = {apr},
number = {4-5},
pages = {705--724},
title = {{Deep learning for detecting robotic grasps}},
url = {http://journals.sagepub.com/doi/10.1177/0278364914549607},
volume = {34},
year = {2013}
}
@inproceedings{VenkatramanNarayanan2017,
abstract = {Reinforcement learning holds the promise of en- abling autonomous robots to learn large repertoires of behav- ioral 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 learn- ing alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off- policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations. I.},
author = {{Venkatraman Narayanan}, Maxim Likhachev},
booktitle = {2017 IEEE International Conference on Robotics and Automation (ICRA) Singapore, May 29 - June 3, 2017},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Venkatraman Narayanan - 2017 - Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates Shixiang.pdf:pdf},
isbn = {9781509046348},
keywords = {Automatisierungstechnik,Computer Vision for Automa},
title = {{Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates Shixiang}},
url = {http://ieeexplore.ieee.org/servlet/opac?punumber=7960754},
year = {2017}
}
@article{Polydoros2017,
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},
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}
}
@inproceedings{He2017,
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},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
doi = {10.1109/ICCV.2017.322},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/He et al. - 2017 - Mask R-CNN.pdf:pdf},
isbn = {9781538610329},
issn = {15505499},
month = {oct},
pages = {2980--2988},
publisher = {IEEE},
title = {{Mask R-CNN}},
url = {http://ieeexplore.ieee.org/document/8237584/},
volume = {2017-Octob},
year = {2017}
}
@article{Chen2019,
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},
month = {apr},
title = {{FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation}},
url = {http://arxiv.org/abs/1904.05729},
year = {2019}
}
@article{Xie2019a,
abstract = {Machine learning techniques have enabled robots to learn narrow, yet complex tasks and also perform broad, yet simple skills with a wide variety of objects. However, learning a model that can both perform complex tasks and generalize to previously unseen objects and goals remains a significant challenge. We study this challenge in the context of "improvisational" tool use: a robot is presented with novel objects and a user-specified goal (e.g., sweep some clutter into the dustpan), and must figure out, using only raw image observations, how to accomplish the goal using the available objects as tools. We approach this problem by training a model with both a visual and physical understanding of multi-object interactions, and develop a sampling-based optimizer that can leverage these interactions to accomplish tasks. We do so by combining diverse demonstration data with self-supervised interaction data, aiming to leverage the interaction data to build generalizable models and the demonstration data to guide the model-based RL planner to solve complex tasks. Our experiments show that our approach can solve a variety of complex tool use tasks from raw pixel inputs, outperforming both imitation learning and self-supervised learning individually. Furthermore, we show that the robot can perceive and use novel objects as tools, including objects that are not conventional tools, while also choosing dynamically to use or not use tools depending on whether or not they are required.},
archivePrefix = {arXiv},
arxivId = {1904.05538},
author = {Xie, Annie and Ebert, Frederik and Levine, Sergey and Finn, Chelsea},
eprint = {1904.05538},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Improvisation through Physical Understanding Using Novel Objects as Tools with Visual Foresight{\_}Xie et al.pdf:pdf},
month = {apr},
title = {{Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight}},
url = {http://arxiv.org/abs/1904.05538},
year = {2019}
}
@article{Wan2019,
abstract = {Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors. Many WSOD approaches adopt multiple instance learning (MIL) and have non-convex loss functions which are prone to get stuck into local minima (falsely localize object parts) while missing full object extent during training. In this paper, we introduce a continuation optimization method into MIL and thereby creating continuation multiple instance learning (C-MIL), with the intention of alleviating the non-convexity problem in a systematic way. We partition instances into spatially related and class related subsets, and approximate the original loss function with a series of smoothed loss functions defined within the subsets. Optimizing smoothed loss functions prevents the training procedure falling prematurely into local minima and facilitates the discovery of Stable Semantic Extremal Regions (SSERs) which indicate full object extent. On the PASCAL VOC 2007 and 2012 datasets, C-MIL improves the state-of-the-art of weakly supervised object detection and weakly supervised object localization with large margins.},
archivePrefix = {arXiv},
arxivId = {1904.05647},
author = {Cvpr, Anonymous and Id, Paper},
eprint = {1904.05647},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}C-MIL Continuation Multiple Instance Learning for Weakly Supervised Object Detection{\_}Cvpr, Id(2).pdf:pdf},
title = {{C-MIL : Continuation Multiple Instance Learning for Weakly Supervised Object Detection}},
url = {http://arxiv.org/abs/1904.05647},
year = {2019}
}
@article{Nittala2018a,
abstract = {Skin-based touch input opens up new opportunities for direct, subtle, and expressive interaction. However, existing skin-worn sensors are restricted to single-touch input and limited by a low resolution. We present the first skin overlay that can capture high-resolution multi-touch input. Our main contributions are: 1) Based on an exploration of functional materials, we present a fabrication approach for printing thin and flexible multi-touch sensors for on-skin interactions. 2) We present the first non-rectangular multi-touch sensor overlay for use on skin and introduce a design tool that generates such sensors in custom shapes and sizes. 3) To validate the feasibility and versatility of our approach, we present four application examples and empirical results from two technical evaluations. They confirm that the sensor achieves a high signal-to-noise ratio on the body under various grounding conditions and has a high spatial accuracy even when subjected to strong deformations.},
author = {Nittala, Aditya Shekhar and Withana, Anusha and Pourjafarian, Narjes and Steimle, J{\"{u}}rgen},
doi = {10.1145/3173574.3173607},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Multi-Touch Skin A Thin and Flexible Multi-Touch Sensor for On-Skin Input{\_}Nittala et al.pdf:pdf},
isbn = {9781450356206},
journal = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18},
pages = {1--12},
title = {{Multi-Touch Skin: A Thin and Flexible Multi-Touch Sensor for On-Skin Input}},
url = {http://dl.acm.org/citation.cfm?doid=3173574.3173607},
year = {2018}
}
@article{Tai2016,
abstract = {Deep learning techniques have been widely applied, achieving state-of-the-art results in various fields of study. This survey focuses on deep learning solutions that target learning control policies for robotics applications. We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning. For deep reinforcement learning (DRL), we begin from traditional reinforcement learning algorithms, showing how they are extended to the deep context and effective mechanisms that could be added on top of the DRL algorithms. We then introduce representative works that utilize DRL to solve navigation and manipulation tasks in robotics. We continue our discussion on methods addressing the challenge of the reality gap for transferring DRL policies trained in simulation to real-world scenarios, and summarize robotics simulation platforms for conducting DRL research. For imitation leaning, we go through its three main categories, behavior cloning, inverse reinforcement learning and generative adversarial imitation learning, by introducing their formulations and their corresponding robotics applications. Finally, we discuss the open challenges and research frontiers.},
archivePrefix = {arXiv},
arxivId = {1612.07139},
author = {Tai, Lei and Zhang, Jingwei and Liu, Ming and Boedecker, Joschka and Burgard, Wolfram},
eprint = {1612.07139},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2016{\_}A Survey of Deep Network Solutions for Learning Control in Robotics From Reinforcement to Imitation{\_}Tai et al.pdf:pdf},
number = {8},
pages = {1--19},
title = {{A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation}},
url = {http://arxiv.org/abs/1612.07139},
volume = {14},
year = {2016}
}
@article{Florence2018a,
abstract = {What is the right object representation for manipulation? We would like robots to visually perceive scenes and learn an understanding of the objects in them that (i) is task-agnostic and can be used as a building block for a variety of manipulation tasks, (ii) is generally applicable to both rigid and non-rigid objects, (iii) takes advantage of the strong priors provided by 3D vision, and (iv) is entirely learned from self-supervision. This is hard to achieve with previous methods: much recent work in grasping does not extend to grasping specific objects or other tasks, whereas task-specific learning may require many trials to generalize well across object configurations or other tasks. In this paper we present Dense Object Nets, which build on recent developments in self-supervised dense descriptor learning, as a consistent object representation for visual understanding and manipulation. We demonstrate they can be trained quickly (approximately 20 minutes) for a wide variety of previously unseen and potentially non-rigid objects. We additionally present novel contributions to enable multi-object descriptor learning, and show that by modifying our training procedure, we can either acquire descriptors which generalize across classes of objects, or descriptors that are distinct for each object instance. Finally, we demonstrate the novel application of learned dense descriptors to robotic manipulation. We demonstrate grasping of specific points on an object across potentially deformed object configurations, and demonstrate using class general descriptors to transfer specific grasps across objects in a class.},
archivePrefix = {arXiv},
arxivId = {1806.08756},
author = {Florence, Peter R. and Manuelli, Lucas and Tedrake, Russ},
eprint = {1806.08756},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Dense Object Nets Learning Dense Visual Object Descriptors By and For Robotic Manipulation{\_}Florence, Manuelli, Tedrake.pdf:pdf},
keywords = {robot manipulation,self-supervision,visual descriptor learning},
pages = {1--12},
title = {{Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation}},
url = {http://arxiv.org/abs/1806.08756},
year = {2018}
}
@article{Wilson2018,
author = {Wilson, Andrew D. and Throm, Nick and Schwarz, Julia and Benko, Hrvoje and Xiao, Robert},
doi = {10.1109/tvcg.2018.2794222},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}MRTouch Adding Touch Input to Head-Mounted Mixed Reality{\_}Wilson et al.pdf:pdf},
issn = {1077-2626},
journal = {IEEE Transactions on Visualization and Computer Graphics},
number = {4},
pages = {1653--1660},
title = {{MRTouch: Adding Touch Input to Head-Mounted Mixed Reality}},
volume = {24},
year = {2018}
}
@article{AndrewBagnell2014a,
abstract = {Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.},
author = {{Andrew Bagnell}, J.},
doi = {10.1007/978-3-319-03194-1_2},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2014{\_}Reinforcement Learning in Robotics A Survey{\_}Andrew Bagnell.pdf:pdf},
issn = {1610742X},
journal = {Springer Tracts in Advanced Robotics},
keywords = {learning control,reinforcement learning,robot,survey},
pages = {9--67},
title = {{Reinforcement Learning in Robotics: A Survey}},
volume = {97},
year = {2014}
}
@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/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;: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},
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{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/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Grasp2Vec Learning Object Representations from Self-Supervised Grasping{\_}Jang et al.pdf:pdf;:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Jang et al. - 2018 - Grasp2Vec Learning Object Representations from Self-Supervised Grasping(2).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{Zhang2018,
abstract = {{\textcopyright} 2018 WILEY-VCH Verlag GmbH  {\&}  Co. KGaA, Weinheim Nanostructured composites built with microporous cellulose fibers and carbon nanotubes (CNTs) have potential impacts in the fields of energy storage, sensors, and flexible electronics. Few results have been shown for high mechanoelectrical sensitivity of CNT-paper composite because of numerous current paths in the network. Here, CNT-paper-based nanostructured composite sensors whose sensitivities are generated by controlled tensile fracture of the composite are presented. Under uniaxial load, the cellulose fibers in the paper experience straightening, stiffening, and fracture. The cellulose fibers originally parallel to the tension are fractured while those inclined and perpendicular to the tension are reorganized to form crossbar junctions in the vicinity of a crack. The cross junctions exhibit resistive and capacitive sensitivity to the out-of-plane force by the compression of the junctions. Such piezoresistive and piezocapacitive sensitivities are characterized and evaluated for human behavior monitoring.},
author = {Zhang, Jinyuan and Lee, Gil Yong and Cerwyn, Chiew and Yang, Jinkyu and Fondjo, Fabrice and Kim, Jong Hoon and Taya, Minoru and Gao, Dayong and Chung, Jae Hyun},
doi = {10.1002/admt.201700266},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Fracture-Induced Mechanoelectrical Sensitivities of Paper-Based Nanocomposites{\_}Zhang et al.pdf:pdf},
issn = {2365709X},
journal = {Advanced Materials Technologies},
keywords = {fracture,multiwall carbon nanotubes,nanocomposites,paper,wearable sensors},
number = {3},
pages = {1--7},
title = {{Fracture-Induced Mechanoelectrical Sensitivities of Paper-Based Nanocomposites}},
volume = {3},
year = {2018}
}
@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/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;: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},
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{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/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Drone-Augmented human vision Exocentric control for drones exploring hidden areas{\_}Erat et al.pdf:pdf;: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},
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{Mavrin2019a,
abstract = {Learning an effective representation for high-dimensional data is a challenging problem in reinforcement learning (RL). Deep reinforcement learning (DRL) such as Deep Q networks (DQN) achieves remarkable success in computer games by learning deeply encoded representation from convolution networks. In this paper, we propose a simple yet very effective method for representation learning with DRL algorithms. Our key insight is that features learned by DRL algorithms are highly correlated, which interferes with learning. By adding a regularized loss that penalizes correlation in latent features (with only slight computation), we decorrelate features represented by deep neural networks incrementally. On 49 Atari games, with the same regularization factor, our decorrelation algorithms perform {\$}70\backslash{\%}{\$} in terms of human-normalized scores, which is {\$}40\backslash{\%}{\$} better than DQN. In particular, ours performs better than DQN on 39 games with 4 close ties and lost only slightly on {\$}6{\$} games. Empirical results also show that the decorrelation method applies to Quantile Regression DQN (QR-DQN) and significantly boosts performance. Further experiments on the losing games show that our decorelation algorithms can win over DQN and QR-DQN with a fined tuned regularization factor.},
archivePrefix = {arXiv},
arxivId = {1903.07765},
author = {Mavrin, Borislav and Yao, Hengshuai and Kong, Linglong},
eprint = {1903.07765},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2019{\_}Deep Reinforcement Learning with Decorrelation{\_}Mavrin, Yao, Kong.pdf:pdf},
month = {mar},
title = {{Deep Reinforcement Learning with Decorrelation}},
url = {http://arxiv.org/abs/1903.07765},
year = {2019}
}
@article{Eslami2018a,
abstract = {Scene representation-the process of converting visual sensory data into concise descriptions-is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.},
archivePrefix = {arXiv},
arxivId = {arXiv:0811.2183v2},
author = {Eslami, S. M.Ali and {Jimenez Rezende}, Danilo and Besse, Frederic and Viola, Fabio and Morcos, Ari S. and Garnelo, Marta and Ruderman, Avraham and Rusu, Andrei A. and Danihelka, Ivo and Gregor, Karol and Reichert, David P. and Buesing, Lars and Weber, Theophane and Vinyals, Oriol and Rosenbaum, Dan and Rabinowitz, Neil and King, Helen and Hillier, Chloe and Botvinick, Matt and Wierstra, Daan and Kavukcuoglu, Koray and Hassabis, Demis},
doi = {10.1126/science.aar6170},
eprint = {arXiv:0811.2183v2},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Neural scene representation and rendering (supplementary material){\_}Eslami et al.pdf:pdf},
isbn = {9783642121425},
issn = {10959203},
journal = {Science (New York, N.Y.)},
number = {6394},
pages = {1204--1210},
pmid = {29903970},
title = {{Neural scene representation and rendering (supplementary material)}},
volume = {360},
year = {2018}
}
@article{Andrychowicz2017a,
abstract = {Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering. It can be combined with an arbitrary off-policy RL algorithm and may be seen as a form of implicit curriculum. We demonstrate our approach on the task of manipulating objects with a robotic arm. In particular, we run experiments on three different tasks: pushing, sliding, and pick-and-place, in each case using only binary rewards indicating whether or not the task is completed. Our ablation studies show that Hindsight Experience Replay is a crucial ingredient which makes training possible in these challenging environments. We show that our policies trained on a physics simulation can be deployed on a physical robot and successfully complete the task.},
archivePrefix = {arXiv},
arxivId = {1707.01495},
author = {Andrychowicz, Marcin and Wolski, Filip and Ray, Alex and Schneider, Jonas and Fong, Rachel and Welinder, Peter and McGrew, Bob and Tobin, Josh and Abbeel, Pieter and Zaremba, Wojciech},
eprint = {1707.01495},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2017{\_}Hindsight Experience Replay{\_}Andrychowicz et al.pdf:pdf},
number = {Nips},
title = {{Hindsight Experience Replay}},
url = {http://arxiv.org/abs/1707.01495},
year = {2017}
}
@article{Lillicrap2015,
abstract = {We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.},
archivePrefix = {arXiv},
arxivId = {1509.02971},
author = {Lillicrap, Timothy P. and Hunt, Jonathan J. and Pritzel, Alexander and Heess, Nicolas and Erez, Tom and Tassa, Yuval and Silver, David and Wierstra, Daan},
eprint = {1509.02971},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2015{\_}Continuous control with deep reinforcement learning{\_}Lillicrap et al.pdf:pdf},
title = {{Continuous control with deep reinforcement learning}},
url = {http://arxiv.org/abs/1509.02971},
year = {2015}
}
@article{Yang2017,
abstract = {Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational auto encoders (VAEs) and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel python library ChemTS that explores the chemical space by combining Monte Carlo tree search (MCTS) and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.},
author = {Yang, Xiufeng and Zhang, Jinzhe and Yoshizoe, Kazuki and Terayama, Kei and Tsuda, Koji},
doi = {10.1080/14686996.2017.1401424},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2017{\_}ChemTS an efficient python library for de novo molecular generation{\_}Yang et al.pdf:pdf},
issn = {18785514},
journal = {Science and Technology of Advanced Materials},
keywords = {Molecular design,Monte Carlo tree search,python library,recurrent neural network},
number = {1},
pages = {972--976},
publisher = {Taylor {\&} Francis},
title = {{ChemTS: an efficient python library for de novo molecular generation}},
url = {https://doi.org/10.1080/14686996.2017.1401424},
volume = {18},
year = {2017}
}
@article{Hessel2017a,
abstract = {The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.},
archivePrefix = {arXiv},
arxivId = {1710.02298},
author = {Hessel, Matteo and Modayil, Joseph and van Hasselt, Hado and Schaul, Tom and Ostrovski, Georg and Dabney, Will and Horgan, Dan and Piot, Bilal and Azar, Mohammad and Silver, David},
eprint = {1710.02298},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2017{\_}Rainbow Combining Improvements in Deep Reinforcement Learning{\_}Hessel et al.pdf:pdf},
title = {{Rainbow: Combining Improvements in Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1710.02298},
year = {2017}
}
@article{Zhang2017a,
abstract = {Figure 1: We present a method for everyday eye contact detection. Our method takes images recorded from an off-the-shelf RGB camera close to a target object or person as input. It combines a state-of-the-art appearance-based gaze estimator with a novel approach for unsupervised gaze target discovery, i.e. without the need for tedious and time-consuming manual data annotation. ABSTRACT Eye contact is an important non-verbal cue in social signal processing and promising as a measure of overt attention in human-object interactions and attentive user interfaces. How-ever, robust detection of eye contact across different users, gaze targets, camera positions, and illumination conditions is notoriously challenging. We present a novel method for eye contact detection that combines a state-of-the-art appearance-based gaze estimator with a novel approach for unsupervised gaze target discovery, i.e. without the need for tedious and time-consuming manual data annotation. We evaluate our method in two real-world scenarios: detecting eye contact at the workplace, including on the main work display, from cameras mounted to target objects, as well as during everyday social interactions with the wearer of a head-mounted egocen-tric camera. We empirically evaluate the performance of our method in both scenarios and demonstrate its effectiveness for detecting eye contact independent of target object type and size, camera position, and user and recording environment.},
author = {Zhang, Xucong and Sugano, Yusuke and Bulling, Andreas},
doi = {10.1145/3126594.3126614},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2017{\_}Everyday Eye Contact Detection Using Unsupervised Gaze Target Discovery{\_}Zhang, Sugano, Bulling.pdf:pdf},
isbn = {9781450349819},
keywords = {Appearance-Based Gaze Estimation,Attentive User Interfaces,Eye Contact,Social Signal Processing},
pages = {193--203},
title = {{Everyday Eye Contact Detection Using Unsupervised Gaze Target Discovery}},
year = {2017}
}
@article{Liu2018a,
abstract = {To restore vision for the blind, several prosthetic approaches have been explored that convey raw images to the brain. So far, these schemes all suffer from a lack of bandwidth. An alternate approach would restore vision at the cognitive level, bypassing the need to convey sensory data. A wearable computer captures video and other data, extracts important scene knowledge, and conveys that to the user in compact form. Here, we implement an intuitive user interface for such a device using augmented reality: each object in the environment has a voice and communicates with the user on command. With minimal training, this system supports many aspects of visual cognition: obstacle avoidance, scene understanding, formation and recall of spatial memories, navigation. Blind subjects can traverse an unfamiliar multi-story building on their first attempt. To spur further development in this domain, we developed an open-source environment for standardized benchmarking of visual assistive devices.},
author = {Liu, Yang and Stiles, Noelle RB and Meister, Markus},
doi = {10.7554/elife.37841},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2018{\_}Augmented reality powers a cognitive assistant for the blind{\_}Liu, Stiles, Meister.pdf:pdf},
journal = {eLife},
pages = {1--41},
title = {{Augmented reality powers a cognitive assistant for the blind}},
volume = {7},
year = {2018}
}
@article{Yahya2017,
abstract = {In principle, reinforcement learning and policy search methods can enable robots to learn highly complex and general skills that may allow them to function amid the complexity and diversity of the real world. However, training a policy that generalizes well across a wide range of real-world conditions requires far greater quantity and diversity of experience than is practical to collect with a single robot. Fortunately, it is possible for multiple robots to share their experience with one another, and thereby, learn a policy collectively. In this work, we explore distributed and asynchronous policy learning as a means to achieve generalization and improved training times on challenging, real-world manipulation tasks. We propose a distributed and asynchronous version of Guided Policy Search and use it to demonstrate collective policy learning on a vision-based door opening task using four robots. We show that it achieves better generalization, utilization, and training times than the single robot alternative.},
archivePrefix = {arXiv},
arxivId = {arXiv:1610.00673v1},
author = {Yahya, Ali and Li, Adrian and Kalakrishnan, Mrinal and Chebotar, Yevgen and Levine, Sergey},
doi = {10.1109/IROS.2017.8202141},
eprint = {arXiv:1610.00673v1},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2017{\_}Collective robot reinforcement learning with distributed asynchronous guided policy search{\_}Yahya et al.pdf:pdf},
isbn = {9781538626825},
issn = {21530866},
journal = {IEEE International Conference on Intelligent Robots and Systems},
pages = {79--86},
title = {{Collective robot reinforcement learning with distributed asynchronous guided policy search}},
volume = {2017-Septe},
year = {2017}
}
@article{Lee2009,
abstract = {This paper proposes an interactive e-learning system using pattern recognition and augmented reality. The goal of proposed system is to provide students with realistic audio-visual contents when they are leaning. The proposed e- learning system consists of image recognition, color and polka-dot pattern recognition, and augmented reality engine with audio-visual contents. When the web camera on a PC captures the current page of textbook, the e-Iearning system first identifies the images on the page, and augments some audio-visual contents on the monitor. For interactive learning, the proposed e-learning system exploits the color-band or polka-dot markers which are stuck to the end of a finger. The color-band and polka-dot marker act like the mouse cursor to indicate the position in the textbook image. Appropriate interactive audio-visual contents are augmented as the marker is located on the predefined image objects in the textbook. The proposed e-learning system was applied to the edu},
author = {Lee, Sang Hwa and Choi, Junyeong and Park, Jong Il and {Sang Hwa}, Lee and Junyeong, Choi and Jong-Il, Park},
doi = {10.1109/TCE.2009.5174470},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Lee et al. - 2009 - Interactive E-Learning System Using Pattern Recognition and Augmented Reality(2).pdf:pdf;:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Lee et al. - 2009 - Interactive E-Learning System Using Pattern Recognition and Augmented Reality(3).pdf:pdf},
issn = {00983063},
journal = {IEEE Transactions on Consumer Electronics},
keywords = {AUDIOVISUAL education,Augmented reality,COMPUTER network resources,E-learning system,INTERACTIVE multimedia,Interactive learning,PATTERN perception,Pattern recognition,STUDENTS -- Services for,WEB-based instruction,augmented reality,interactive learning,pattern recognition},
number = {2},
pages = {883--890},
title = {{Interactive E-Learning System Using Pattern Recognition and Augmented Reality.}},
url = {http://search-1ebscohost-1com-1ebsco.han.buw.uw.edu.pl/login.aspx?direct=true{\&}db=a9h{\&}AN=43924116{\&}lang=pl{\&}site=ehost-live},
volume = {55},
year = {2009}
}
@article{Tian2019a,
abstract = {Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dynamics models. We propose deep tactile MPC, a framework for learning to perform tactile servoing from raw tactile sensor inputs, without manual supervision. We show that this method enables a robot equipped with a GelSight-style tactile sensor to manipulate a ball, analog stick, and 20-sided die, learning from unsupervised autonomous interaction and then using the learned tactile predictive model to reposition each object to user-specified configurations, indicated by a goal tactile reading. Videos, visualizations and the code are available here: https://sites.google.com/view/deeptactilempc},
archivePrefix = {arXiv},
arxivId = {1903.04128},
author = {Tian, Stephen and Ebert, Frederik and Jayaraman, Dinesh and Mudigonda, Mayur and Finn, Chelsea and Calandra, Roberto and Levine, Sergey},
eprint = {1903.04128},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper//2019{\_}Manipulation by Feel Touch-Based Control with Deep Predictive Models{\_}Tian et al.pdf:pdf},
month = {mar},
title = {{Manipulation by Feel: Touch-Based Control with Deep Predictive Models}},
url = {http://arxiv.org/abs/1903.04128},
year = {2019}
}
@article{YannLeCunLeonBottouYoshuaBengio1998,
author = {{Yann LeCun, Leon Bottou, Yoshua Bengio}, Partrick Haffner},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/1998{\_}Gradient-Based Learning Applied to Document Recognition{\_}Yann LeCun, Leon Bottou, Yoshua Bengio.pdf:pdf},
journal = {proc. OF THE IEEE},
title = {{Gradient-Based Learning Applied to Document Recognition}},
year = {1998}
}
@article{He2015,
abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57{\%} error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28{\%} relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC {\&} COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
archivePrefix = {arXiv},
arxivId = {1512.03385},
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
eprint = {1512.03385},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}Deep Residual Learning for Image Recognition{\_}He et al.pdf:pdf},
pages = {1--9},
title = {{Deep Residual Learning for Image Recognition}},
url = {http://arxiv.org/abs/1512.03385},
year = {2015}
}
@article{Gu2017a,
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},
isbn = {9781509046331},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
pages = {3389--3396},
pmid = {26774160},
title = {{Deep Reinforcement Learning for Robotic Manipulation}},
year = {2017}
}
@article{Nishi2017a,
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/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{Sabour2017a,
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/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{RonaldJ.WilliamsandDavidZipser1989,
archivePrefix = {arXiv},
arxivId = {1404.7828},
author = {{Ronald J. Williams and David Zipser}},
doi = {10.1016/j.neunet.2014.09.003},
eprint = {1404.7828},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2014{\_}Deep Learning in Neural Networks{\_}Ronald J. Williams and David Zipser.pdf:pdf},
isbn = {0893-6080},
issn = {18792782},
journal = {Neural Computation},
number = {No. 2},
pages = {270--280},
pmid = {25462637},
title = {{Deep Learning in Neural Networks}},
volume = {1},
year = {2014}
}
@article{Durugkar2016,
abstract = {Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the Atari domain. In this paper, we explore output representation modeling in the form of temporal abstraction to improve convergence and reliability of deep reinforcement learning approaches. We concentrate on macro-actions, and evaluate these on different Atari 2600 games, where we show that they yield significant improvements in learning speed. Additionally, we show that they can even achieve better scores than DQN. We offer analysis and explanation for both convergence and final results, revealing a problem deep RL approaches have with sparse reward signals.},
archivePrefix = {arXiv},
arxivId = {1606.04615},
author = {Durugkar, Ishan P. and Rosenbaum, Clemens and Dernbach, Stefan and Mahadevan, Sridhar},
eprint = {1606.04615},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Deep Reinforcement Learning With Macro-Actions{\_}Durugkar et al.pdf:pdf},
pages = {2094--2100},
title = {{Deep Reinforcement Learning With Macro-Actions}},
url = {http://arxiv.org/abs/1606.04615},
year = {2016}
}
@article{Levine2018c,
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/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},
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{Lee2018a,
abstract = {joonhyub.lee | sang-gyun.an | yongkwan.kim | seokhyung.bae @ kaist.ac.kr Figure 1. In a mock setup depicting the flow of Projective Windows, (a) the user wishing to adjust the position and scale of an AR window (b) grabs the window, (c) moves it, (d) makes it bigger by bringing it closer, and (e) projects it to the desired position. ABSTRACT In augmented and virtual reality (AR and VR), there may be many 3D planar windows with 2D texts, images, and videos on them. However, managing the position, orientation, and scale of such a window in an immersive 3D workspace can be difficult. Projective Windows strategically uses the absolute and apparent sizes of the window at various stages of the interaction to enable the grabbing, moving, scaling, and releasing of the window in one continuous hand gesture. With it, the user can quickly and intuitively manage and interact with windows in space without any controller hardware or dedicated widget. Through an evaluation, we demonstrate that our technique is performant and preferable, and that projective geometry plays an important role in the design of spatial user interfaces.},
author = {Lee, Joon Hyub and An, Sang-Gyun and Kim, Yongkwan and Bae, Seok-Hyung},
doi = {10.1145/3170427.3186524},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Projective Windows Bringing Windows in Space to the Fingertip{\_}Lee et al.pdf:pdf},
isbn = {978-1-4503-5621-3},
journal = {Proc. of CHI},
keywords = {3d window management,augmented reality,virtual reality},
pages = {D412:1----D412:1},
title = {{Projective Windows: Bringing Windows in Space to the Fingertip}},
url = {http://doi.acm.org/10.1145/3170427.3186524},
year = {2018}
}
@article{Karambakhsh2019a,
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 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},
number = {October 2017},
pages = {328--336},
title = {{Deep gesture interaction for augmented anatomy learning}},
volume = {45},
year = {2019}
}
@book{Francois-Lavet2018,
abstract = {Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.},
archivePrefix = {arXiv},
arxivId = {1811.12560},
author = {Francois-Lavet, Vincent and Henderson, Peter and Islam, Riashat and Bellemare, Marc G. and Pineau, Joelle},
doi = {10.1561/2200000071},
eprint = {1811.12560},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}An Introduction to Deep Reinforcement Learning{\_}Francois-Lavet et al.pdf:pdf},
isbn = {9783319994925},
keywords = {deep learning,deep reinforcement learning,machine learning,reinforcement learning},
pages = {298--328},
title = {{An Introduction to Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1811.12560{\%}0Ahttp://dx.doi.org/10.1561/2200000071},
year = {2018}
}
@article{Kimura2018a,
abstract = {We propose a system, called ExtVision, to augment visual experiences by generating and projecting context-images onto the periphery of the television or computer screen. A peripheral projection of the context-image is one of the most effective techniques to enhance visual experiences. However, the projection is not commonly used at present, because of the difficulty in preparing the context-image. In this paper, we propose a deep neural network-based method to generate context-images for peripheral projection. A user study was performed to investigate the manner in which the proposed system augments traditional visual experiences. In addition, we present applications and future prospects of the developed system.},
author = {Kimura, Naoki and Rekimoto, Jun},
doi = {10.1145/3173574.3174001},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}ExtVision Aumentation of Visual Experiences with Generation of Context Images for Peripheral Vision Using Deep Neural Network{\_}Kimur.pdf:pdf},
isbn = {9781450356206},
journal = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18},
pages = {1--10},
title = {{ExtVision: Aumentation of Visual Experiences with Generation of Context Images for Peripheral Vision Using Deep Neural Network}},
url = {http://dl.acm.org/citation.cfm?doid=3173574.3174001},
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/Library/Application Support/Mendeley Desktop/Downloaded/OpenAI et al. - 2018 - Learning Dexterous In-Hand Manipulation.pdf:pdf},
pages = {1--27},
title = {{Learning Dexterous In-Hand Manipulation}},
url = {http://arxiv.org/abs/1808.00177},
year = {2018}
}
@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/Library/Application Support/Mendeley Desktop/Downloaded/Ha, Schmidhuber - 2018 - Recurrent World Models Facilitate Policy Evolution(2).pdf:pdf},
month = {sep},
title = {{Recurrent World Models Facilitate Policy Evolution}},
url = {http://arxiv.org/abs/1809.01999},
year = {2018}
}
@book{Agostinelli2018,
author = {Agostinelli, Forest and Hocquet, Guillaume and Singh, Sameer and B, Pierre Baldi},
doi = {10.1007/978-3-319-99492-5},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Braverman Readings in Machine Learning. Key Ideas from Inception to Current State{\_}Agostinelli et al.pdf:pdf},
isbn = {978-3-319-99491-8},
keywords = {Machine learning,Reinforcement learning,Deep learn,deep learning,deep reinforcement learning,machine learning,reinforcement learning},
pages = {298--328},
publisher = {Springer International Publishing},
title = {{Braverman Readings in Machine Learning. Key Ideas from Inception to Current State}},
url = {http://link.springer.com/10.1007/978-3-319-99492-5},
volume = {11100},
year = {2018}
}
@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},
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{Kimura2018,
abstract = {We propose a system, called ExtVision, to augment visual experiences by generating and projecting context-images onto the periphery of the television or computer screen. A peripheral projection of the context-image is one of the most effective techniques to enhance visual experiences. However, the projection is not commonly used at present, because of the difficulty in preparing the context-image. In this paper, we propose a deep neural network-based method to generate context-images for peripheral projection. A user study was performed to investigate the manner in which the proposed system augments traditional visual experiences. In addition, we present applications and future prospects of the developed system.},
author = {Kimura, Naoki and Rekimoto, Jun},
doi = {10.1145/3173574.3174001},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}ExtVision Augmentation of Visual Experiences with Generation of Context Images for Peripheral Vision Using Deep Neural Network{\_}Kimu.pdf:pdf},
isbn = {9781450356206},
pages = {1--10},
title = {{ExtVision: Augmentation of Visual Experiences with Generation of Context Images for Peripheral Vision Using Deep Neural Network}},
year = {2018}
}
@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},
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{Hembury2003,
abstract = {How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of naturally occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness to occlusion derive from the role that recurrent connectivity and recurrent processing mechanisms play in the model. Furthermore, this interaction of recurrent connectivity and learning predicts that high-level visual representations should be shaped by error signals from nearby, associated brain areas over the course of visual learning. Consistent with this prediction, we show how semantic knowledge about object categories changes the nature of their learned visual representations, as well as how this representational shift supports the mapping between perceptual and conceptual knowledge. Altogether, these findings support the potential importance of ongoing recurrent processing throughout the brain's visual system and suggest ways in which object recognition can be understood in terms of interactions within and between processes over time.},
archivePrefix = {arXiv},
arxivId = {1512.03385},
author = {Hembury, Guy A. and Borovkov, Victor V. and Lintuluoto, Juha M. and Inoue, Yoshihisa},
doi = {10.3389/fpsyg.2013.00124},
eprint = {1512.03385},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2003{\_}Deep Residual Learning for Image Recognition Kaiming{\_}Hembury et al.pdf:pdf},
isbn = {978-1-4673-6964-0},
issn = {1664-1078},
journal = {Chemistry Letters},
keywords = {2003,32,5,a -oxo,an acid,anti conformational switching in,base controlled molecular switch,chemistry letters vol,no,syn},
number = {5},
pages = {428--429},
pmid = {23554596},
title = {{Deep Residual Learning for Image Recognition Kaiming}},
url = {http://joi.jlc.jst.go.jp/JST.JSTAGE/cl/2003.428?from=CrossRef},
volume = {32},
year = {2003}
}
@article{.2554,
author = {久保田遥},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Learning Deep Features for Discriminative Localization{\_}久保田遥.pdf:pdf},
title = {{Learning Deep Features for Discriminative Localization}},
year = {2016}
}
@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},
title = {{Learning Word-Like Units from Joint Audio-Visual Analysis}},
url = {http://arxiv.org/abs/1701.07481},
year = {2017}
}
@article{Lee2018,
author = {Lee, Joon Hyub and An, Sang-Gyun and Kim, Yongkwan and Bae, Seok-Hyung},
doi = {10.1145/3170427.3186524},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Projective Windows Bringing Windows in Space to the Fingertip{\_}Lee et al(2).pdf:pdf},
isbn = {9781450356206},
pages = {1--1},
title = {{Projective Windows: Bringing Windows in Space to the Fingertip}},
year = {2018}
}
@article{Silver2016,
abstract = {The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8{\%} winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.},
author = {Silver, David and Huang, Aja and Maddison, Chris J and Guez, Arthur and Sifre, Laurent and van den Driessche, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and Kavukcuoglu, Koray and Graepel, Thore and Hassabis, Demis},
doi = {10.1038/nature16961},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Silver et al. - 2016 - Mastering the game of Go with deep neural networks and tree search(2).pdf:pdf},
issn = {1476-4687},
journal = {Nature},
number = {7587},
pages = {484--9},
pmid = {26819042},
publisher = {Nature Publishing Group},
title = {{Mastering the game of Go with deep neural networks and tree search.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26819042},
volume = {529},
year = {2016}
}
@article{Lee2009,
abstract = {This paper proposes an interactive e-learning system using pattern recognition and augmented reality. The goal of proposed system is to provide students with realistic audio-visual contents when they are leaning. The proposed e-learning system consists of image recognition, color and polka-dot pattern recognition, and augmented reality engine with audio-visual contents. When the Web camera on a PC captures the current page of textbook, the e-learning system first identifies the images on the page, and augments some audio-visual contents on the monitor. For interactive learning, the proposed e-learning system exploits the color-band or polka-dot markers which are stuck to the end of a finger. The color-band and polka-dot marker act like the mouse cursor to indicate the position in the textbook image. Appropriate interactive audio-visual contents are augmented as the marker is located on the predefined image objects in the textbook. The proposed e-learning system was applied to the educational courses in the elementary school, and we obtained satisfactory results for real applications. We expect that the proposed e-learning system is popular when the educational contents and scenarios are sufficiently provided.},
author = {Lee, Sang Hwa and Choi, Junyeong and Park, Jong Il},
doi = {10.1109/TCE.2009.5174470},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Lee et al. - 2009 - Interactive E-Learning System Using Pattern Recognition and Augmented Reality(3).pdf:pdf},
issn = {00983063},
journal = {IEEE Transactions on Consumer Electronics},
keywords = {Augmented reality,E-learning system,Interactive learning,Pattern recognition},
number = {2},
pages = {883--890},
title = {{Interactive e-learning system using pattern recognition and augmented reality}},
volume = {55},
year = {2009}
}
@article{Schmidhuber2015,
abstract = {In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning {\&} evolutionary computation, and indirect search for short programs encoding deep and large networks.},
author = {Schmidhuber, J{\"{u}}rgen},
doi = {10.1016/j.neunet.2014.09.003},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}Deep Learning in neural networks An overview{\_}Schmidhuber.pdf:pdf},
issn = {18792782},
journal = {Neural Networks},
keywords = {Deep learning,Evolutionary computation,Reinforcement learning,Supervised learning,Unsupervised learning},
pages = {85--117},
publisher = {Elsevier Ltd},
title = {{Deep Learning in neural networks: An overview}},
url = {http://dx.doi.org/10.1016/j.neunet.2014.09.003},
volume = {61},
year = {2015}
}
@article{Hasselt20016,
abstract = {Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the Atari domain. In this paper, we explore output representation modeling in the form of temporal abstraction to improve convergence and reliability of deep reinforcement learning approaches. We concentrate on macro-actions, and evaluate these on different Atari 2600 games, where we show that they yield significant improvements in learning speed. Additionally, we show that they can even achieve better scores than DQN. We offer analysis and explanation for both convergence and final results, revealing a problem deep RL approaches have with sparse reward signals.},
archivePrefix = {arXiv},
arxivId = {1606.04615},
author = {Durugkar, Ishan P. and Rosenbaum, Clemens and Dernbach, Stefan and Mahadevan, Sridhar},
eprint = {1606.04615},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Double DQN.pdf{\_}Hasselt, Guez, Silver.pdf:pdf},
journal = {Aaai},
keywords = {Technical Papers: Machine Learning Methods},
pages = {2094--2100},
title = {{Deep Reinforcement Learning With Macro-Actions}},
url = {http://arxiv.org/abs/1606.04615},
year = {2016}
}
@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.},
author = {Levine, Sergey and Pastor, Peter and Krizhevsky, Alex and Ibarz, Julian and Quillen, Deirdre},
doi = {10.1177/0278364917710318},
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.{\_}Internation{\_}ijrr.pdf:pdf},
issn = {17413176},
journal = {International Journal of Robotics Research},
keywords = {Robotics,deep learning,neural networks},
number = {4-5},
pages = {421--436},
title

repository: yumion/master-thesis

repo_name: yumion/master-thesis