text:
= {{Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection}},
volume = {37},
year = {2018}
}
@article{Sankai2019,
author = {Toyama, Hiroaki and Kawamoto, Hiroaki and Sankai, Yoshiyuki},
doi = {10.1109/embc.2019.8857106},
file = {:Users/atsushi/Downloads/08857106.pdf:pdf},
isbn = {9781538613115},
issn = {1557170X},
journal = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages = {1645--1650},
publisher = {IEEE},
title = {{Development of Cybernic Robot Arm to realize Support Action cooperated with Hemiplegic Person's Arm}},
year = {2019}
}
@article{HSR2019,
abstract = {There has been an increasing interest in mobile manipulators that are capable of performing physical work in living spaces worldwide, corresponding to an aging population with declining birth rates with the expectation of improving quality of life (QoL). Research and development is a must in intelligent sensing and software which will enable advanced recognition, judgment, and motion to realize household work by robots. In order to accelerate this research, we have developed a compact and safe research platform, Human Support Robot (HSR), which can be operated in an actual home environment. We assume that overall R{\&}D will accelerate by using a common robot platform among many researchers since that enables them to share their research results. Currently, the number of HSR users is expanding to 33 sites in 8 countries worldwide (as of February 15, 2018). Software and technical knowledge of all users is shared through a community website. HSR has been adopted as a standard platform for international robot competitions such as RoboCup@Home and World Robot Summit (WRS). HSR is provided to participants of those competitions through public offering. In this paper, we describe HSR's development background, and technical detail of its hardware and software. Specifically, we describe its omnidirectional mobile base using the dual-wheel caster-drive mechanism, which is the basis of HSR's operational movement and a novel whole body motion control system. Finally, we describe the results of utilization in RoboCup@Home and field tests in order to demonstrate the effect of introducing the platform.},
author = {Yamamoto, Takashi and Terada, Koji and Ochiai, Akiyoshi and Saito, Fuminori and Asahara, Yoshiaki and Murase, Kazuto},
doi = {10.1186/s40648-019-0132-3},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Yamamoto et al. - 2019 - Development of Human Support Robot as the research platform of a domestic mobile manipulator.pdf:pdf},
issn = {21974225},
journal = {ROBOMECH Journal},
keywords = {Domestic robots,Mobile manipulation,Motion and path planning},
number = {1},
title = {{Development of Human Support Robot as the research platform of a domestic mobile manipulator}},
volume = {6},
year = {2019}
}
@article{Toyama2018a,
abstract = {{\textcopyright} 2017 IEEE. Hemiplegic persons have difficulties in handling many kinds of tasks in daily life because they must perform the tasks by only the unaffected arm and hand. In order to realize upper limb work support for hemiplegic persons, a robot arm that has ability to grip various daily necessities and cooperate with the nonparalyzed side in same work space is required. The purposes of this study is to develop a novel assistance system "My Cybernic Robot Arm" that has measurement function and adjustment function of grip force, and to confirm the basic performance of the system through the benchmark experiments. The system is composed of a robot arm and hand, a sensing unit and a control unit. The system has grip force adjustment function and learning function of grip force by grip force teaching playback. The robot arm has six degrees of freedom and was designed with minimum size to cover an area of the table tasks so as not to narrow the work area of the nonparalyzed side. The sensing unit can measure the grip force and centroid position of grip force calculated by using a capacitance feature. In addition, developed sensing unit is small enough to be embedded in the fingertip of robot hand. We conducted verification of the sensing unit accuracy and grip force teaching and reproduction as basic experiments to confirm the basic performance of the system. The results showed that the system could measure grip force and load center of gravity position. In addition, it was confirmed that the system could learn the appropriate grip force and adjust the grip force by grip force teaching playback. In conclusion, the system has basic performance because it can grip the object with appropriate grip force. This system is expected to support upper limb work of hemiplegic persons such as desk work in everyday life, and improve QOL (Quality of life) and encourage independence.},
author = {Toyama, Hiroaki and Kawamoto, Hiroaki and Sankai, Yoshiyuki},
doi = {10.1109/ROBIO.2017.8324526},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Basic research of upper limb work support system My cybernic robot arm for hemiplegic persons{\_}Toyama, Kawamoto, Sankai.pdf:pdf},
isbn = {9781538637418},
journal = {2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017},
keywords = {Grip force control,Grip force teaching,Load center of gravity position,Nonuniform load},
pages = {1--7},
title = {{Basic research of upper limb work support system "My cybernic robot arm" for hemiplegic persons}},
volume = {2018-Janua},
year = {2018}
}
@article{Weidner2013,
abstract = {Due to the volatile conditions in today's production, flexible assembly systems are required. However, current plants are often custom-made which are designed for a fix product spectrum. Especially for the assembly of unique products or products with a small-scale lot size and large-scale lot size with a high diversity of variants, many manufacturing steps are difficult or non economic to automate. Thus, a large part of the manufacturing processes is to be performed manually. These can be supported by assistance systems, but appropriate systems are seldom available. A new approach for supporting manual assembly tasks is the hybridization of biological and technical systems, a so-called "Human Hybrid Robot" (HHR). The kinematic chains of human, machines and tools are configured task-depended in serial and parallel arrangement. By doing so, the individual skills are used optimally. Main focus of this concept is improving the assembly accuracy and error prevention to boost the overall quality of the assembly processes. This paper presents the theoretical concept. Possible applications and realization of exemplary components are outlined to show the potential of HHR. {\textcopyright} 2013 German Academic Society for Production Engineering (WGP).},
author = {Weidner, R. and Kong, N. and Wulfsberg, J. P.},
doi = {10.1007/s11740-013-0487-x},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Weidner, Kong, Wulfsberg - 2013 - Human Hybrid Robot A new concept for supporting manual assembly tasks.pdf:pdf},
issn = {09446524},
journal = {Production Engineering},
keywords = {Assembly,Assistance system,HHR,Human robot integration,Human-robot-cooperation/interaction,Hybrid kinematics},
number = {6},
pages = {675--684},
title = {{Human Hybrid Robot: A new concept for supporting manual assembly tasks}},
volume = {7},
year = {2013}
}
@article{リハビリテーション,
abstract = {Rehabilitation using myoelectric prosthesis for trans-radial amputees has become wide spread and well established in several developed countries. However, the clinical use of myoelectric prostheses for trans-radial amputees has not yet spread in Japan. It is well known that once amputees become accustomed to using their prosthesis efficiently through adequate reha- bilitation, that various activities which the amputees had given up so far will become possible through enhanced bimanual activities. Although myoelectric prostheses have proved to be useful, the majority of amputees have not been satisfied with their function. As an amputee becomes a better user, they request not only simple tasks but also complicated ones. As a consequence, the amputee comes to know the limits of their myoelectric prosthesis, thus expectations for superior prostheses will arise. The recent remarkable development of engineering technology has enabled the progress of prosthetic limb technology, leading to the production of far superior functional prostheses which meet the user's expectations. However, there is a paradox in developing such superior prostheses. The more advanced the prosthesis we produce, the higher the cost. To achieve this end, it is absolutely imperative to secure the cooperation of both clinicians and engi- neers. Furthermore, a rehabilitation strategy for patients with a higher level of amputation （trans- humeral amputation, shoulder disarticulation） remains unsolved. In this paper, we propose a “Hybrid Myoelectric Prosthesis”, which consists of a myoelectric hand as a terminal device and a body-powered active elbow joint, as a realistic solution for higher level amputees. In addition, we introduce Targeted Reinnervation （TR） as a future strategy for reference.},
author = {陳, 隆明},
doi = {10.2490/jjrmc.49.31},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/CHIN - 2012 - Myoelectric Prostheses Current Status and Problems to be Solved, a Rehabilitation Strategy for Higher Level Amputation (2).pdf:pdf},
issn = {1881-3526},
journal = {The Japanese Journal of Rehabilitation Medicine},
number = {1},
pages = {31--36},
title = {筋電義手普及の現状と課題，高位切断者に対する戦略，そして今後の展望},
volume = {49},
year = {2012}
}
@article{fasterRCNN,
abstract = {State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3], our detection system has a frame rate of 5 fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.},
archivePrefix = {arXiv},
arxivId = {1506.01497},
author = {Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
doi = {10.1109/TPAMI.2016.2577031},
eprint = {1506.01497},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Ren et al. - 2017 - Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks.pdf:pdf},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Object detection,convolutional neural network,region proposal},
month = {jun},
number = {6},
pages = {1137--1149},
pmid = {27295650},
publisher = {IEEE Computer Society},
title = {{Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks}},
volume = {39},
year = {2017}
}
@article{Yamamoto2019b,
abstract = {There has been an increasing interest in mobile manipulators that are capable of performing physical work in living spaces worldwide, corresponding to an aging population with declining birth rates with the expectation of improving quality of life (QoL). We assume that overall research and development will accelerate by using a common robot platform among a lot of researchers since that enables them to share their research results. Therefore we have developed a compact and safe research platform, Human Support Robot (HSR), which can be operated in an actual home environment and we have provided it to various research institutes to establish the developers community. Currently, the number of HSR users is expanding to 44 sites in 12 countries worldwide (as of November 30th, 2018). To activate the community, we assume that the robot competition will be effective. As a result of international public offering, HSR has been adopted as a standard platform for international robot competitions such as RoboCup@Home and World Robot Summit (WRS). HSR is provided to participants of those competitions. In this paper, we describe HSR's development background since 2006, and technical detail of hardware design and software architecture. Specifically, we describe its omnidirectional mobile base using the dual-wheel caster-drive mechanism, which is the basis of HSR's operational movement and a novel whole body motion control system. Finally, we describe the verification of autonomous task capability and the results of utilization in RoboCup@Home in order to demonstrate the effect of introducing the platform.},
author = {Yamamoto, Takashi and Terada, Koji and Ochiai, Akiyoshi and Saito, Fuminori and Asahara, Yoshiaki and Murase, Kazuto},
doi = {10.1186/s40648-019-0132-3},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Development of the Research Platform of a Domestic Mobile Manipulator Utilized for International Competition and Field Test{\_}Yama(2).pdf:pdf},
issn = {21974225},
journal = {ROBOMECH Journal},
keywords = {Domestic robots,Mobile manipulation,Motion and path planning},
number = {1},
title = {{Development of Human Support Robot as the research platform of a domestic mobile manipulator}},
volume = {6},
year = {2019}
}
@article{Yamamoto2019,
abstract = {There has been an increasing interest in mobile manipulators that are capable of performing physical work in living spaces worldwide, corresponding to an aging population with declining birth rates with the expectation of improving quality of life (QoL). Research and development is a must in intelligent sensing and software which will enable advanced recognition, judgment, and motion to realize household work by robots. In order to accelerate this research, we have developed a compact and safe research platform, Human Support Robot (HSR), which can be operated in an actual home environment. We assume that overall R{\&}D will accelerate by using a common robot platform among many researchers since that enables them to share their research results. Currently, the number of HSR users is expanding to 33 sites in 8 countries worldwide (as of February 15, 2018). Software and technical knowledge of all users is shared through a community website. HSR has been adopted as a standard platform for international robot competitions such as RoboCup@Home and World Robot Summit (WRS). HSR is provided to participants of those competitions through public offering. In this paper, we describe HSR's development background, and technical detail of its hardware and software. Specifically, we describe its omnidirectional mobile base using the dual-wheel caster-drive mechanism, which is the basis of HSR's operational movement and a novel whole body motion control system. Finally, we describe the results of utilization in RoboCup@Home and field tests in order to demonstrate the effect of introducing the platform.},
author = {Yamamoto, Takashi and Terada, Koji and Ochiai, Akiyoshi and Saito, Fuminori and Asahara, Yoshiaki and Murase, Kazuto},
doi = {10.1109/IROS.2018.8593798},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Yamamoto et al. - 2019 - Development of the Research Platform of a Domestic Mobile Manipulator Utilized for International Competition an.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Development of the Research Platform of a Domestic Mobile Manipulator Utilized for International Competition and Field Test{\_}Yamamot.pdf:pdf},
isbn = {9781538680940},
issn = {21530866},
journal = {IEEE International Conference on Intelligent Robots and Systems},
keywords = {Artificial Intelligence,Computational Intelligence,Control and Systems Theory,Mechatronics,Robotics and Automation},
month = {dec},
number = {1},
pages = {7675--7682},
publisher = {SpringerOpen},
title = {{Development of the Research Platform of a Domestic Mobile Manipulator Utilized for International Competition and Field Test}},
url = {https://robomechjournal.springeropen.com/articles/10.1186/s40648-019-0132-3},
volume = {6},
year = {2018}
}
@article{Yamamoto2019,
abstract = {There has been an increasing interest in mobile manipulators that are capable of performing physical work in living spaces worldwide, corresponding to an aging population with declining birth rates with the expectation of improving quality of life (QoL). Research and development is a must in intelligent sensing and software which will enable advanced recognition, judgment, and motion to realize household work by robots. In order to accelerate this research, we have developed a compact and safe research platform, Human Support Robot (HSR), which can be operated in an actual home environment. We assume that overall R{\&}D will accelerate by using a common robot platform among many researchers since that enables them to share their research results. Currently, the number of HSR users is expanding to 33 sites in 8 countries worldwide (as of February 15, 2018). Software and technical knowledge of all users is shared through a community website. HSR has been adopted as a standard platform for international robot competitions such as RoboCup@Home and World Robot Summit (WRS). HSR is provided to participants of those competitions through public offering. In this paper, we describe HSR's development background, and technical detail of its hardware and software. Specifically, we describe its omnidirectional mobile base using the dual-wheel caster-drive mechanism, which is the basis of HSR's operational movement and a novel whole body motion control system. Finally, we describe the results of utilization in RoboCup@Home and field tests in order to demonstrate the effect of introducing the platform.},
author = {Yamamoto, Takashi and Terada, Koji and Ochiai, Akiyoshi and Saito, Fuminori and Asahara, Yoshiaki and Murase, Kazuto},
doi = {10.1109/iros.2018.8593798},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Yamamoto et al. - 2019 - Development of the Research Platform of a Domestic Mobile Manipulator Utilized for International Competition an.pdf:pdf},
issn = {2197-4225},
journal = {ROBOMECH Journal},
keywords = {Artificial Intelligence,Computational Intelligence,Control and Systems Theory,Mechatronics,Robotics and Automation},
month = {dec},
number = {1},
pages = {7675--7682},
publisher = {SpringerOpen},
title = {{Development of the Research Platform of a Domestic Mobile Manipulator Utilized for International Competition and Field Test}},
url = {https://robomechjournal.springeropen.com/articles/10.1186/s40648-019-0132-3},
volume = {6},
year = {2019}
}
@article{Fan2018,
abstract = {In this paper, we present a decentralized sensor-level collision avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent's steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy's robustness and effectiveness. We validate the learned sensor-level collision avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller's robustness against the sim-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution to the safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. Videos are available at https://sites.google.com/view/hybridmrca},
archivePrefix = {arXiv},
arxivId = {1808.03841},
author = {Fan, Tingxiang and Long, Pinxin and Liu, Wenxi and Pan, Jia},
eprint = {1808.03841},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Fan et al. - 2018 - Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation.pdf:pdf},
month = {aug},
title = {{Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios}},
url = {http://arxiv.org/abs/1808.03841},
year = {2018}
}
@article{Zeng2018,
abstract = {This article presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses an object-agnostic grasping framework to map from visual observations to actions: inferring dense pixel-wise probability maps of the affordances for four different grasping primitive actions. It then executes the action with the highest affordance and recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional data collection or re-training. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT–Princeton Team system that took first place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu/},
archivePrefix = {arXiv},
arxivId = {1710.01330},
author = {Zeng, Andy and Song, Shuran and Yu, Kuan Ting and Donlon, Elliott and Hogan, Francois R. and Bauza, Maria and Ma, Daolin and Taylor, Orion and Liu, Melody and Romo, Eudald and Fazeli, Nima and Alet, Ferran and Dafle, Nikhil Chavan and Holladay, Rachel and Morena, Isabella and {Qu Nair}, Prem and Green, Druck and Taylor, Ian and Liu, Weber and Funkhouser, Thomas and Rodriguez, Alberto},
doi = {10.1109/ICRA.2018.8461044},
eprint = {1710.01330},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Zeng et al. - 2017 - Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching.pdf:pdf},
isbn = {9781538630815},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
month = {oct},
pages = {3750--3757},
title = {{Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching}},
url = {http://arxiv.org/abs/1710.01330},
year = {2018}
}
@inproceedings{Sankai2017,
abstract = {{\textcopyright} 2017 IEEE. Hemiplegic persons have difficulties in handling many kinds of tasks in daily life because they must perform the tasks by only the unaffected arm and hand. In order to realize upper limb work support for hemiplegic persons, a robot arm that has ability to grip various daily necessities and cooperate with the nonparalyzed side in same work space is required. The purposes of this study is to develop a novel assistance system "My Cybernic Robot Arm" that has measurement function and adjustment function of grip force, and to confirm the basic performance of the system through the benchmark experiments. The system is composed of a robot arm and hand, a sensing unit and a control unit. The system has grip force adjustment function and learning function of grip force by grip force teaching playback. The robot arm has six degrees of freedom and was designed with minimum size to cover an area of the table tasks so as not to narrow the work area of the nonparalyzed side. The sensing unit can measure the grip force and centroid position of grip force calculated by using a capacitance feature. In addition, developed sensing unit is small enough to be embedded in the fingertip of robot hand. We conducted verification of the sensing unit accuracy and grip force teaching and reproduction as basic experiments to confirm the basic performance of the system. The results showed that the system could measure grip force and load center of gravity position. In addition, it was confirmed that the system could learn the appropriate grip force and adjust the grip force by grip force teaching playback. In conclusion, the system has basic performance because it can grip the object with appropriate grip force. This system is expected to support upper limb work of hemiplegic persons such as desk work in everyday life, and improve QOL (Quality of life) and encourage independence.},
author = {Toyama, Hiroaki and Kawamoto, Hiroaki and Sankai, Yoshiyuki},
booktitle = {2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017},
doi = {10.1109/ROBIO.2017.8324526},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Toyama, Kawamoto, Sankai - 2018 - Basic research of upper limb work support system My cybernic robot arm for hemiplegic persons(2).pdf:pdf},
isbn = {9781538637418},
keywords = {Grip force control,Grip force teaching,Load center of gravity position,Nonuniform load},
month = {mar},
pages = {1--7},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Basic research of upper limb work support system "My cybernic robot arm" for hemiplegic persons}},
volume = {2018-Janua},
year = {2018}
}
@article{Barreto2016,
abstract = {Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scenario where the reward function changes between tasks but the environment's dynamics remain the same. Our approach rests on two key ideas: "successor features", a value function representation that decouples the dynamics of the environment from the rewards, and "generalized policy improvement", a generalization of dynamic programming's policy improvement operation that considers a set of policies rather than a single one. Put together, the two ideas lead to an approach that integrates seamlessly within the reinforcement learning framework and allows the free exchange of information across tasks. The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place. We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.},
archivePrefix = {arXiv},
arxivId = {1606.05312},
author = {Barreto, Andr{\'{e}} and Dabney, Will and Munos, R{\'{e}}mi and Hunt, Jonathan J. and Schaul, Tom and van Hasselt, Hado and Silver, David},
eprint = {1606.05312},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Barreto et al. - 2016 - Successor Features for Transfer in Reinforcement Learning.pdf:pdf},
month = {jun},
title = {{Successor Features for Transfer in Reinforcement Learning}},
url = {http://arxiv.org/abs/1606.05312},
year = {2016}
}
@article{Zeng2018a,
abstract = {Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. In this work, we demonstrate that it is possible to discover and learn these synergies from scratch through model-free deep reinforcement learning. Our method involves training two fully convolutional networks that map from visual observations to actions: one infers the utility of pushes for a dense pixel-wise sampling of end effector orientations and locations, while the other does the same for grasping. Both networks are trained jointly in a Q-learning framework and are entirely self-supervised by trial and error, where rewards are provided from successful grasps. In this way, our policy learns pushing motions that enable future grasps, while learning grasps that can leverage past pushes. During picking experiments in both simulation and real-world scenarios, we find that our system quickly learns complex behaviors amid challenging cases of clutter, and achieves better grasping success rates and picking efficiencies than baseline alternatives after only a few hours of training. We further demonstrate that our method is capable of generalizing to novel objects. Qualitative results (videos), code, pre-trained models, and simulation environments are available at http://vpg.cs.princeton.edu},
archivePrefix = {arXiv},
arxivId = {1803.09956},
author = {Zeng, Andy and Song, Shuran and Welker, Stefan and Lee, Johnny and Rodriguez, Alberto and Funkhouser, Thomas},
doi = {10.1109/IROS.2018.8593986},
eprint = {1803.09956},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Zeng et al. - 2018 - Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning.pdf:pdf},
isbn = {9781538680940},
issn = {21530866},
journal = {IEEE International Conference on Intelligent Robots and Systems},
month = {mar},
pages = {4238--4245},
title = {{Learning Synergies between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning}},
url = {http://arxiv.org/abs/1803.09956},
year = {2018}
}
@article{Fu2019,
abstract = {Recently two-stage detectors have surged ahead of single-shot detectors in the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are immensely popular in embedded vision applications. This paper brings single-shot detectors up to the same level as current two-stage techniques. We do this by improving training for the state-of-the-art single-shot detector, RetinaNet, in three ways: integrating instance mask prediction for the first time, making the loss function adaptive and more stable, and including additional hard examples in training. We call the resulting augmented network RetinaMask. The detection component of RetinaMask has the same computational cost as the original RetinaNet, but is more accurate. COCO test-dev results are up to 41.4 mAP for RetinaMask-101 vs 39.1mAP for RetinaNet-101, while the runtime is the same during evaluation. Adding Group Normalization increases the performance of RetinaMask-101 to 41.7 mAP. Code is at:https://github.com/chengyangfu/retinamask},
archivePrefix = {arXiv},
arxivId = {1901.03353},
author = {Fu, Cheng-Yang and Shvets, Mykhailo and Berg, Alexander C.},
eprint = {1901.03353},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Fu, Shvets, Berg - 2019 - RetinaMask Learning to predict masks improves state-of-the-art single-shot detection for free.pdf:pdf},
month = {jan},
title = {{RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free}},
url = {http://arxiv.org/abs/1901.03353},
year = {2019}
}
@article{Quillen2018,
abstract = {In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been successfully applied to a range of challenging environments, but the proliferation of algorithms makes it difficult to discern which particular approach would be best suited for a rich, diverse task like grasping. To answer this question, we propose a simulated benchmark for robotic grasping that emphasizes off-policy learning and generalization to unseen objects. Off-policy learning enables utilization of grasping data over a wide variety of objects, and diversity is important to enable the method to generalize to new objects that were not seen during training. We evaluate the benchmark tasks against a variety of Q-function estimation methods, a method previously proposed for robotic grasping with deep neural network models, and a novel approach based on a combination of Monte Carlo return estimation and an off-policy correction. Our results indicate that several simple methods provide a surprisingly strong competitor to popular algorithms such as double Q-learning, and our analysis of stability sheds light on the relative tradeoffs between the algorithms.},
archivePrefix = {arXiv},
arxivId = {1802.10264},
author = {Quillen, Deirdre and Jang, Eric and Nachum, Ofir and Finn, Chelsea and Ibarz, Julian and Levine, Sergey},
doi = {10.1109/ICRA.2018.8461039},
eprint = {1802.10264},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Quillen et al. - 2018 - Deep Reinforcement Learning for Vision-Based Robotic Grasping A Simulated Comparative Evaluation of Off-Policy M.pdf:pdf},
isbn = {9781538630815},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
month = {feb},
pages = {6284--6291},
title = {{Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods}},
url = {http://arxiv.org/abs/1802.10264},
year = {2018}
}
@article{Redmon2015a,
abstract = {We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques. The model outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU. Our network can simultaneously perform classification so that in a single step it recognizes the object and finds a good grasp rectangle. A modification to this model predicts multiple grasps per object by using a locally constrained prediction mechanism. The locally constrained model performs significantly better, especially on objects that can be grasped in a variety of ways.},
author = {Redmon, Joseph and Angelova, Anelia},
doi = {10.1109/ICRA.2015.7139361},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2015{\_}Real-time grasp detection using convolutional neural networks{\_}Redmon, Angelova.pdf:pdf},
isbn = {9781479969234},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
number = {June},
pages = {1316--1322},
title = {{Real-time grasp detection using convolutional neural networks}},
volume = {2015-June},
year = {2015}
}
@article{Pinto2015,
abstract = {— Current learning-based robot grasping ap-proaches exploit human-labeled datasets for training the mod-els. 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},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Supersizing self-supervision Learning to grasp from 50K tries and 700 robot hours{\_}Pinto, Gupta.pdf:pdf},
isbn = {9781467380263},
journal = {arXiv preprint arXiv:1509.06825},
pages = {3406--3413},
title = {{Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours}},
year = {2015}
}
@article{Quigley2007,
abstract = {The STanford Artificial Intelligence Robot (STAIR) project is a long-term group effort aimed at producing a viable home and office assistant robot. As a small concrete step towards this goal, we showed a demonstration video at the 2007 AAAI Mobile Robot Exhibition of the STAIR 1 robot responding to a verbal command to fetch an item. Carrying out this task involved the integration of multiple components, including spoken dialog, navigation, computer visual object detection, and robotic grasping. This paper describes the hardware and software integration frameworks used to facilitate the development of these components and to bring them together for the demonstration. Copyright {\textcopyright} 2007, American Association for Artificial Intelligence.},
author = {Quigley, Morgan and Berger, Eric and Ng, AY},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Wang et al. - 2007 - STAIR Hardware and Software Architecture.pdf:pdf},
journal = {AAAI, Robotics Workshop},
keywords = {AAAI Technical Report WS-07-15},
pages = {31--37},
title = {{Stair: Hardware and software architecture}},
url = {http://www.aaai.org/Papers/Workshops/2007/WS-07-15/WS07-15-008.pdf},
year = {2007}
}
@article{Images2016,
author = {Images, Using Depth and Engineering, Mathematical Information and Engineering, Mathematical Information},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}深度画像を用いたロボットアームによる 平面上未知物体の把持方法{\_}Images, Engineering, Engineering.pdf:pdf},
pages = {235--236},
title = {深度画像を用いたロボットアームによる 平面上未知物体の把持方法},
year = {2016}
}
% Encoding: UTF-8

@article{Sankai2017,
    abstract = {{\textcopyright} 2017 IEEE. Hemiplegic persons have difficulties in handling many kinds of tasks in daily life because they must perform the tasks by only the unaffected arm and hand. In order to realize upper limb work support for hemiplegic persons, a robot arm that has ability to grip various daily necessities and cooperate with the nonparalyzed side in same work space is required. The purposes of this study is to develop a novel assistance system "My Cybernic Robot Arm" that has measurement function and adjustment function of grip force, and to confirm the basic performance of the system through the benchmark experiments. The system is composed of a robot arm and hand, a sensing unit and a control unit. The system has grip force adjustment function and learning function of grip force by grip force teaching playback. The robot arm has six degrees of freedom and was designed with minimum size to cover an area of the table tasks so as not to narrow the work area of the nonparalyzed side. The sensing unit can measure the grip force and centroid position of grip force calculated by using a capacitance feature. In addition, developed sensing unit is small enough to be embedded in the fingertip of robot hand. We conducted verification of the sensing unit accuracy and grip force teaching and reproduction as basic experiments to confirm the basic performance of the system. The results showed that the system could measure grip force and load center of gravity position. In addition, it was confirmed that the system could learn the appropriate grip force and adjust the grip force by grip force teaching playback. In conclusion, the system has basic performance because it can grip the object with appropriate grip force. This system is expected to support upper limb work of hemiplegic persons such as desk work in everyday life, and improve QOL (Quality of life) and encourage independence.},
    author = {Toyama, Hiroaki and Kawamoto, Hiroaki and Sankai, Yoshiyuki},
    doi = {10.1109/ROBIO.2017.8324526},
    file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}Basic research of upper limb work support system My cybernic robot arm for hemiplegic persons{\_}Toyama, Kawamoto, Sankai.pdf:pdf},
    isbn = {9781538637418},
    journal = {2017 IEEE Int. Conf. Robot. Biomimetics, ROBIO 2017},
    keywords = {Grip force control,Grip force teaching,Load center of gravity position,Nonuniform load},
    mendeley-groups = {RobotHand},
    pages = {1--7},
    title = {{Basic research of upper limb work support system "My cybernic robot arm" for hemiplegic persons}},
    volume = {2018-Janua},
    year = {2018}
}

@article{Sankai2019,
    author = {Toyama, Hiroaki and Kawamoto, Hiroaki and Sankai, Yoshiyuki},
    doi = {10.1109/embc.2019.8857106},
    file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2019{\_}Development of Cybernic Robot Arm to realize Support Action cooperated with Hemiplegic Person's Arm{\_}Toyama, Kawamoto, Sankai.pdf:pdf},
    isbn = {9781538613115},
    issn = {1557170X},
    journal = {2019 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.},
    mendeley-groups = {RobotHand},
    pages = {1645--1650},
    publisher = {IEEE},
    title = {{Development of Cybernic Robot Arm to realize Support Action cooperated with Hemiplegic Person's Arm}},
    year = {2019}
}

@article{リハビリテーション,
    abstract = {Rehabilitation using myoelectric prosthesis for trans-radial amputees has become wide spread and well established in several developed countries. However, the clinical use of myoelectric prostheses for trans-radial amputees has not yet spread in Japan. It is well known that once amputees become accustomed to using their prosthesis efficiently through adequate reha- bilitation, that various activities which the amputees had given up so far will become possible through enhanced bimanual activities. Although myoelectric prostheses have proved to be useful, the majority of amputees have not been satisfied with their function. As an amputee becomes a better user, they request not only simple tasks but also complicated ones. As a consequence, the amputee comes to know the limits of their myoelectric prosthesis, thus expectations for superior prostheses will arise. The recent remarkable development of engineering technology has enabled the progress of prosthetic limb technology, leading to the production of far superior functional prostheses which meet the user's expectations. However, there is a paradox in developing such superior prostheses. The more advanced the prosthesis we produce, the higher the cost. To achieve this end, it is absolutely imperative to secure the cooperation of both clinicians and engi- neers. Furthermore, a rehabilitation strategy for patients with a higher level of amputation （trans- humeral amputation, shoulder disarticulation） remains unsolved. In this paper, we propose a “Hybrid Myoelectric Prosthesis”, which consists of a myoelectric hand as a terminal device and a body-powered active elbow joint, as a realistic solution for higher level amputees. In addition, we introduce Targeted Reinnervation （TR） as a future strategy for reference.},
    author = {陳, 隆明},
    doi = {10.2490/jjrmc.49.31},
    file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2012{\_}Myoelectric Prostheses Current Status and Problems to be Solved, a Rehabilitation Strategy for Higher Level Amputation Patients,(2).pdf:pdf},
    issn = {1881-3526},
    journal = {Japanese J. Rehabil. Med.},
    mendeley-groups = {RobotHand},
    number = {1},
    pages = {31--36},
    title = {筋電義手普及の現状と課題，高位切断者に対する戦略，そして今後の展望},
    volume = {49},
    year = {2012}
}
@article{kawamura2001,
author = {川村, 次郎},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2001{\_}筋電義手の普及への展望{\_}川村.pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2001{\_}筋電義手の普及への展望{\_}川村.pdf:pdf},
journal = {日本義肢装具学会誌},
mendeley-groups = {RobotHand},
number = {4},
title = {筋電義手の普及への展望},
volume = {17},
year = {2001}
}

@inproceedings{HSR2018,
    author = {Yamamoto, Takashi and Terada, Koji and Ochiai, Akiyoshi and Saito, Fuminori and Asahara, Yoshiaki and Murase, Kazuto},
    year = {2018},
    month = {10},
    pages = {7675-7682},
    title = {Development of the Research Platform of a Domestic Mobile Manipulator Utilized for International Competition and Field Test},
    doi = {10.1109/IROS.2018.8593798}
}

@article{sekitani2016ultraflexible,
    title={Ultraflexible organic amplifier with biocompatible gel electrodes},
    author={Sekitani, Tsuyoshi and Yokota, Tomoyuki and Kuribara, Kazunori and Kaltenbrunner, Martin and Fukushima, Takanori and Inoue, Yusuke and Sekino, Masaki and Isoyama, Takashi and Abe, Yusuke and Onodera, Hiroshi and others},
    journal={Nature communications},
    volume={7},
    pages={11425},
    year={2016},
    publisher={Nature Publishing Group}
}

@article{esteva2017dermatologist,
    title={Dermatologist-level classification of skin cancer with deep neural networks},
    author={Esteva, Andre and Kuprel, Brett and Novoa, Roberto A and Ko, Justin and Swetter, Susan M and Blau, Helen M and Thrun, Sebastian},
    journal={Nature},
    volume={542},
    number={7639},
    pages={115},
    year={2017},
    publisher={Nature Publishing Group}
}

@article{wang2016deep,
    title={Deep learning for identifying metastatic breast cancer},
    author={Wang, Dayong and Khosla, Aditya and Gargeya, Rishab and Irshad, Humayun and Beck, Andrew H},
    journal={arXiv preprint arXiv:1606.05718},
    year={2016}
}

@article{rathore2015novel,
    title={Novel structural descriptors for automated colon cancer detection and grading},
    author={Rathore, Saima and Hussain, Mutawarra and Iftikhar, Muhammad Aksam and Jalil, Abdul},
    journal={Computer methods and programs in biomedicine},
    volume={121},
    number={2},
    pages={92--108},
    year={2015},
    publisher={Elsevier}
}

@article{fmri,
    added-at = {2018-08-13T00:00:00.000+0200},
    author = {Sarraf, Saman and Tofighi, Ghassem},
    biburl = {https://www.bibsonomy.org/bibtex/2f76303c736027c896b06d5f237ee0647/dblp},
    ee = {http://arxiv.org/abs/1603.08631},
    interhash = {754c41d33f9793be141dacdabc9d4b9e},
    intrahash = {f76303c736027c896b06d5f237ee0647},
    journal = {CoRR},
    keywords = {dblp},
    timestamp = {2018-08-14T14:48:39.000+0200},
    title = {Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks.},
    url = {http://dblp.uni-trier.de/db/journals/corr/corr1603.html#SarrafT16},
    volume = {abs/1603.08631},
    year = 2016
}

@article{litjens2017survey,
    title={A survey on deep learning in medical image analysis},
    author={Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and Van Der Laak, Jeroen Awm and Van Ginneken, Bram and S{\'a}nchez, Clara I},
    journal={Medical image analysis},
    volume={42},
    pages={60--88},
    year={2017},
    publisher={Elsevier}
}

@article{cho2014learning,
    title={Learning phrase representations using RNN encoder-decoder for statistical machine translation},
    author={Cho, Kyunghyun and Van Merri{\"e}nboer, Bart and Gulcehre, Caglar and Bahdanau, Dzmitry and Bougares, Fethi and Schwenk, Holger and Bengio, Yoshua},
    journal={arXiv preprint arXiv:1406.1078},
    year={2014}
}

@inproceedings{Donahue_2015_CVPR,
    added-at = {2015-10-16T00:00:00.000+0200},
    author = {Donahue, Jeff and Hendricks, Lisa Anne and Guadarrama, Sergio and Rohrbach, Marcus and Venugopalan, Subhashini and Darrell, Trevor and Saenko, Kate},
    biburl = {https://www.bibsonomy.org/bibtex/2637bb837c6e53a7ffd53730013b73117/dblp},
    booktitle = {CVPR},

    ee = {http://dx.doi.org/10.1109/CVPR.2015.7298878},
    interhash = {0b1e8c68fcc14312dfc5770e77d708c4},
    intrahash = {637bb837c6e53a7ffd53730013b73117},
    isbn = {978-1-4673-6964-0},
    keywords = {dblp},
    pages = {2625-2634},
    publisher = {IEEE Computer Society},
    timestamp = {2016-04-29T11:54:14.000+0200},
    title = {Long-term recurrent convolutional networks for visual recognition and description.},
    url = {http://dblp.uni-trier.de/db/conf/cvpr/cvpr2015.html#DonahueHGRVDS15},
    year = 2015
}

@inproceedings{wang2016image,
    title={Image captioning with deep bidirectional LSTMs},
    author={Wang, Cheng and Yang, Haojin and Bartz, Christian and Meinel, Christoph},
    booktitle={Proceedings of the 2016 ACM on Multimedia Conference},
    pages={988--997},
    year={2016},
    organization={ACM}
}

@article{ji20133d,
    title={3D convolutional neural networks for human action recognition},
    author={Ji, Shuiwang and Xu, Wei and Yang, Ming and Yu, Kai},
    journal={IEEE transactions on pattern analysis and machine intelligence},
    volume={35},
    number={1},
    pages={221--231},
    year={2013},
    publisher={IEEE}
}

@article{hinton2006reducing,
    title={Reducing the dimensionality of data with neural networks},
    author={Hinton, Geoffrey E and Salakhutdinov, Ruslan R},
    journal={science},
    volume={313},
    number={5786},
    pages={504--507},
    year={2006},
    publisher={American Association for the Advancement of Science}
}

@article{kingma2013auto,
    added-at = {2018-08-13T00:00:00.000+0200},
    author = {Kingma, Diederik P. and Welling, Max},
    biburl = {https://www.bibsonomy.org/bibtex/2486ad13a443259d137cb57be1dc77002/dblp},
    ee = {http://arxiv.org/abs/1312.6114},
    interhash = {85731e0fbdb10b8543ea9f55301b37a5},
    intrahash = {486ad13a443259d137cb57be1dc77002},
    journal = {CoRR},
    keywords = {dblp},
    timestamp = {2018-08-14T14:45:20.000+0200},
    title = {Auto-Encoding Variational Bayes.},
    url = {http://dblp.uni-trier.de/db/journals/corr/corr1312.html#KingmaW13},
    volume = {abs/1312.6114},
    year = 2013
}

@inproceedings{abbasnejad2017infinite,
    title={Infinite variational autoencoder for semi-supervised learning},
    author={Abbasnejad, M Ehsan and Dick, Anthony and van den Hengel, Anton},
    booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    pages={781--790},
    year={2017},
    organization={IEEE}
}

@inproceedings{narayanaswamy2017learning,
    title={Learning disentangled representations with semi-supervised deep generative models},
    author={Narayanaswamy, Siddharth and Paige, T Brooks and Van de Meent, Jan-Willem and Desmaison, Alban and Goodman, Noah and Kohli, Pushmeet and Wood, Frank and Torr, Philip},
    booktitle={Advances in Neural Information Processing Systems},
    pages={5925--5935},
    year={2017}
}
@Article{Erat2018,
    author    = {Okan Erat and Werner Alexander Isop and Denis Kalkofen and Dieter Schmalstieg},
    title     = {Drone-Augmented Human Vision: Exocentric Control for Drones Exploring Hidden Areas},
    journal   = {{IEEE} Transactions on Visualization and Computer Graphics},
    year      = {2018},
    volume    = {24},
    number    = {4},
    pages     = {1437--1446},
    month     = {apr},
    doi       = {10.1109/tvcg.2018.2794058},
    file      = {:08260942.pdf:PDF},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Article{Karambakhsh2018,
    author    = {Ahmad Karambakhsh and Aouaidjia Kamel and Bin Sheng and Ping Li and Po Yang and David Dagan Feng},
    title     = {Deep gesture interaction for augmented anatomy learning},
    journal   = {Int. J. Inf. Manage.},
    year      = {2018},
    month     = {mar},
    doi       = {10.1016/j.ijinfomgt.2018.03.004},
    publisher = {Elsevier {BV}},
}

@Article{OpenAI2018,
    author        = {OpenAI},
    title         = {Learning Dexterous In-Hand Manipulation},
    journal       = {CoRR},
    year          = {2018},
    volume        = {abs/1808.00177},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1808-00177},
    eprint        = {1808.00177},
    timestamp     = {Sun, 02 Sep 2018 15:01:56 +0200},
    url           = {http://arxiv.org/abs/1808.00177},
}

@Article{Levine2016,
    author        = {Sergey Levine and Peter Pastor and Alex Krizhevsky and Deirdre Quillen},
    title         = {Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection},
    journal       = {CoRR},
    year          = {2016},
    volume        = {abs/1603.02199},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/LevinePKQ16},
    eprint        = {1603.02199},
    timestamp     = {Mon, 13 Aug 2018 16:48:48 +0200},
    url           = {http://arxiv.org/abs/1603.02199},
}

@Article{Kober2013,
    author    = {Jens Kober and J. Andrew Bagnell and Jan Peters},
    title     = {Reinforcement learning in robotics: A survey},
    journal   = {The International Journal of Robotics Research},
    year      = {2013},
    volume    = {32},
    number    = {11},
    pages     = {1238--1274},
    month     = {aug},
    doi       = {10.1177/0278364913495721},
    publisher = {{SAGE} Publications},
}

@Article{Li2017,
    author        = {Yuxi Li},
    title         = {Deep Reinforcement Learning: An Overview},
    journal       = {CoRR},
    year          = {2017},
    volume        = {abs/1701.07274},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/Li17b},
    eprint        = {1701.07274},
    timestamp     = {Mon, 13 Aug 2018 16:48:40 +0200},
    url           = {http://arxiv.org/abs/1701.07274},
}

@Article{Yahya2016,
    author        = {Ali Yahya and Adrian Li and Mrinal Kalakrishnan and Yevgen Chebotar and Sergey Levine},
    title         = {Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search},
    journal       = {CoRR},
    year          = {2016},
    volume        = {abs/1610.00673},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/YahyaLKCL16},
    eprint        = {1610.00673},
    timestamp     = {Mon, 13 Aug 2018 16:46:51 +0200},
    url           = {http://arxiv.org/abs/1610.00673},
}

@inproceedings{shotton2008semantic,
    title={Semantic texton forests for image categorization and segmentation},
    author={Shotton, Jamie and Johnson, Matthew and Cipolla, Roberto},
    booktitle={Computer vision and pattern recognition, 2008. CVPR 2008. IEEE Conference on},
    pages={1--8},
    year={2008},
    organization={IEEE}
}

@inproceedings{kontschieder2011structured,
    title={Structured class-labels in random forests for semantic image labelling},
    author={Kontschieder, Peter and Bulo, Samuel Rota and Bischof, Horst and Pelillo, Marcello},
    booktitle={Computer Vision (ICCV), 2011 IEEE International Conference on},
    pages={2190--2197},
    year={2011},
    organization={IEEE}
}

@Article{Levine2015,
    author        = {Sergey Levine and Chelsea Finn and Trevor Darrell and Pieter Abbeel},
    title         = {End-to-End Training of Deep Visuomotor Policies},
    journal       = {CoRR},
    year          = {2015},
    volume        = {abs/1504.00702},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/LevineFDA15},
    eprint        = {1504.00702},
    timestamp     = {Mon, 13 Aug 2018 16:47:04 +0200},
    url           = {http://arxiv.org/abs/1504.00702},
}

@Article{alphaGoZero,
    author    = {David Silver and Julian Schrittwieser and Karen Simonyan and Ioannis Antonoglou and Aja Huang and Arthur Guez and Thomas Hubert and Lucas Baker and Matthew Lai and Adrian Bolton and Yutian Chen and Timothy Lillicrap and Fan Hui and Laurent Sifre and George van den Driessche and Thore Graepel and Demis Hassabis},
    title     = {Mastering the game of Go without human knowledge},
    journal   = {Nature},
    year      = {2017},
    volume    = {550},
    number    = {7676},
    pages     = {354--359},
    month     = {oct},
    doi       = {10.1038/nature24270},
    publisher = {Springer Nature},
}

@Article{Jaderberg2018,
    author        = {Max Jaderberg and Wojciech M. Czarnecki and Iain Dunning and Luke Marris and Guy Lever and Antonio Garc{\'{\i}}a Casta{\~{n}}eda and Charles Beattie and Neil C. Rabinowitz and Ari S. Morcos and Avraham Ruderman and Nicolas Sonnerat and Tim Green and Louise Deason and Joel Z. Leibo and David Silver and Demis Hassabis and Koray Kavukcuoglu and Thore Graepel},
    title         = {Human-level performance in first-person multiplayer games with population-based deep reinforcement learning},
    journal       = {CoRR},
    year          = {2018},
    volume        = {abs/1807.01281},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1807-01281},
    eprint        = {1807.01281},
    timestamp     = {Mon, 13 Aug 2018 16:48:08 +0200},
    url           = {http://arxiv.org/abs/1807.01281},
}

@Article{DQNnature,
    author    = {Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Andrei A. Rusu and Joel Veness and Marc G. Bellemare and Alex Graves and Martin Riedmiller and Andreas K. Fidjeland and Georg Ostrovski and Stig Petersen and Charles Beattie and Amir Sadik and Ioannis Antonoglou and Helen King and Dharshan Kumaran and Daan Wierstra and Shane Legg and Demis Hassabis},
    title     = {Human-level control through deep reinforcement learning},
    journal   = {Nature},
    year      = {2015},
    volume    = {518},
    number    = {7540},
    pages     = {529--533},
    month     = {feb},
    doi       = {10.1038/nature14236},
    publisher = {Springer Nature},
}

@Article{DQN,
    author        = {Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Alex Graves and Ioannis Antonoglou and Daan Wierstra and Martin A. Riedmiller},
    title         = {Playing Atari with Deep Reinforcement Learning},
    journal       = {CoRR},
    year          = {2013},
    volume        = {abs/1312.5602},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/MnihKSGAWR13},
    eprint        = {1312.5602},
    timestamp     = {Mon, 13 Aug 2018 16:47:42 +0200},
    url           = {http://arxiv.org/abs/1312.5602},
}

@Article{Schmidhuber2015,
    author    = {Jürgen Schmidhuber},
    title     = {Deep learning in neural networks: An overview},
    journal   = {Neural Networks},
    year      = {2015},
    volume    = {61},
    pages     = {85--117},
    month     = {jan},
    doi       = {10.1016/j.neunet.2014.09.003},
    publisher = {Elsevier {BV}},
}

@Article{alphaZero,
    author        = {David Silver and Thomas Hubert and Julian Schrittwieser and Ioannis Antonoglou and Matthew Lai and Arthur Guez and Marc Lanctot and Laurent Sifre and Dharshan Kumaran and Thore Graepel and Timothy P. Lillicrap and Karen Simonyan and Demis Hassabis},
    title         = {Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm},
    journal       = {CoRR},
    year          = {2017},
    volume        = {abs/1712.01815},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1712-01815},
    eprint        = {1712.01815},
    timestamp     = {Mon, 13 Aug 2018 16:46:01 +0200},
    url           = {http://arxiv.org/abs/1712.01815},
}

@Article{alphaGo,
    author    = {David Silver and Aja Huang and Chris J. Maddison and Arthur Guez and Laurent Sifre and George van den Driessche and Julian Schrittwieser and Ioannis Antonoglou and Veda Panneershelvam and Marc Lanctot and Sander Dieleman and Dominik Grewe and John Nham and Nal Kalchbrenner and Ilya Sutskever and Timothy Lillicrap and Madeleine Leach and Koray Kavukcuoglu and Thore Graepel and Demis Hassabis},
    title     = {Mastering the game of Go with deep neural networks and tree search},
    journal   = {Nature},
    year      = {2016},
    volume    = {529},
    number    = {7587},
    pages     = {484--489},
    month     = {jan},
    doi       = {10.1038/nature16961},
    publisher = {Springer Nature},
}

@Article{Levine2017,
    author    = {Sergey Levine and Peter Pastor and Alex Krizhevsky and Julian Ibarz and Deirdre Quillen},
    title     = {Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection},
    journal   = {The International Journal of Robotics Research},
    year      = {2017},
    volume    = {37},
    number    = {4-5},
    pages     = {421--436},
    month     = {jun},
    doi       = {10.1177/0278364917710318},
    publisher = {{SAGE} Publications},
}

@Article{Lample2016,
    author        = {Guillaume Lample and Devendra Singh Chaplot},
    title         = {Playing {FPS} Games with Deep Reinforcement Learning},
    journal       = {CoRR},
    year          = {2016},
    volume        = {abs/1609.05521},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/LampleC16},
    eprint        = {1609.05521},
    timestamp     = {Mon, 13 Aug 2018 16:47:19 +0200},
    url           = {http://arxiv.org/abs/1609.05521},
}

@Article{DoubleQ,
    author        = {Hado van Hasselt and Arthur Guez and David Silver},
    title         = {Deep Reinforcement Learning with Double Q-learning},
    journal       = {CoRR},
    year          = {2015},
    volume        = {abs/1509.06461},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/HasseltGS15},
    eprint        = {1509.06461},
    timestamp     = {Mon, 13 Aug 2018 16:47:32 +0200},
    url           = {http://arxiv.org/abs/1509.06461},
}

@Article{Hausknecht2015,
    author        = {Matthew J. Hausknecht and Peter Stone},
    title         = {Deep Recurrent Q-Learning for Partially Observable MDPs},
    journal       = {CoRR},
    year          = {2015},
    volume        = {abs/1507.06527},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/HausknechtS15},
    eprint        = {1507.06527},
    timestamp     = {Mon, 13 Aug 2018 16:48:38 +0200},
    url           = {http://arxiv.org/abs/1507.06527},
}

@Article{Hu2017,
    author        = {Ronghang Hu and Piotr Doll{\'{a}}r and Kaiming He and Trevor Darrell and Ross B. Girshick},
    title         = {Learning to Segment Every Thing},
    journal       = {CoRR},
    year          = {2017},
    volume        = {abs/1711.10370},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1711-10370},
    eprint        = {1711.10370},
    timestamp     = {Mon, 13 Aug 2018 16:47:48 +0200},
    url           = {http://arxiv.org/abs/1711.10370},
}

@Article{Harwath2017,
    author        = {David F. Harwath and James R. Glass},
    title         = {Learning Word-Like Units from Joint Audio-Visual Analysis},
    journal       = {CoRR},
    year          = {2017},
    volume        = {abs/1701.07481},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/HarwathG17},
    eprint        = {1701.07481},
    timestamp     = {Mon, 13 Aug 2018 16:45:59 +0200},
    url           = {http://arxiv.org/abs/1701.07481},
}

@Article{Tai2016,
    author        = {Lei Tai and Ming Liu},
    title         = {Deep-learning in Mobile Robotics - from Perception to Control Systems: {A} Survey on Why and Why not},
    journal       = {CoRR},
    year          = {2016},
    volume        = {abs/1612.07139},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/TaiL16a},
    eprint        = {1612.07139},
    timestamp     = {Mon, 13 Aug 2018 16:46:14 +0200},
    url           = {http://arxiv.org/abs/1612.07139},
}

@Article{Denil2016,
    author        = {Misha Denil and Pulkit Agrawal and Tejas D. Kulkarni and Tom Erez and Peter Battaglia and Nando de Freitas},
    title         = {Learning to Perform Physics Experiments via Deep Reinforcement Learning},
    journal       = {CoRR},
    year          = {2016},
    volume        = {abs/1611.01843},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/DenilAKEBF16},
    eprint        = {1611.01843},
    timestamp     = {Mon, 13 Aug 2018 16:46:40 +0200},
    url           = {http://arxiv.org/abs/1611.01843},
}

@Misc{mudigonda2018investigating,
    author = {Mayur Mudigonda and Pulkit Agrawal and Michael Deweese and Jitendra Malik},
    title  = {Investigating Deep Reinforcement Learning For Grasping Objects With An Anthropomorphic Hand},
    year   = {2018},
    url    = {https://openreview.net/forum?id=BJZt4KywG},
}

@Article{Rainbow,
    author        = {Matteo Hessel and Joseph Modayil and Hado van Hasselt and Tom Schaul and Georg Ostrovski and Will Dabney and Daniel Horgan and Bilal Piot and Mohammad Gheshlaghi Azar and David Silver},
    title         = {Rainbow: Combining Improvements in Deep Reinforcement Learning},
    journal       = {CoRR},
    year          = {2017},
    volume        = {abs/1710.02298},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1710-02298},
    eprint        = {1710.02298},
    timestamp     = {Mon, 13 Aug 2018 16:48:05 +0200},
    url           = {http://arxiv.org/abs/1710.02298},
}

@InProceedings{ResNet,
    author    = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
    title     = {Deep Residual Learning for Image Recognition},
    booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
    year      = {2016},
    month     = {jun},
    publisher = {{IEEE}},
    doi       = {10.1109/cvpr.2016.90},
}

@Article{Eslami2018,
    author    = {S. M. Ali Eslami and Danilo Jimenez Rezende and Frederic Besse and Fabio Viola and Ari S. Morcos and Marta Garnelo and Avraham Ruderman and Andrei A. Rusu and Ivo Danihelka and Karol Gregor and David P. Reichert and Lars Buesing and Theophane Weber and Oriol Vinyals and Dan Rosenbaum and Neil Rabinowitz and Helen King and Chloe Hillier and Matt Botvinick and Daan Wierstra and Koray Kavukcuoglu and Demis Hassabis},
    title     = {Neural scene representation and rendering},
    journal   = {Science},
    year      = {2018},
    volume    = {360},
    number    = {6394},
    pages     = {1204--1210},
    month     = {jun},
    doi       = {10.1126/science.aar6170},
    publisher = {American Association for the Advancement of Science ({AAAS})},
}

@Article{Sabour2017,
    author        = {Sara Sabour and Nicholas Frosst and Geoffrey E. Hinton},
    title         = {Dynamic Routing Between Capsules},
    journal       = {CoRR},
    year          = {2017},
    volume        = {abs/1710.09829},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1710-09829},
    eprint        = {1710.09829},
    timestamp     = {Mon, 13 Aug 2018 16:47:11 +0200},
    url           = {http://arxiv.org/abs/1710.09829},
}

@InProceedings{AlexNet,
    author    = {Alex Krizhevsky and Ilya Sutskever and Geoffrey E. Hinton},
    title     = {Imagenet classification with deep convolutional neural networks},
    booktitle = {Advances in Neural Information Processing Systems},
    year      = {2012},
    pages     = {2012},
}

@InProceedings{GoogLeNet,
    author    = {Christian Szegedy and Wei Liu and Yangqing Jia and Pierre Sermanet and Scott Reed and Dragomir Anguelov and Dumitru Erhan and Vincent Vanhoucke and Andrew Rabinovich},
    title     = {Going deeper with convolutions},
    booktitle = {2015 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
    year      = {2015},
    month     = {jun},
    publisher = {{IEEE}},
    doi       = {10.1109/cvpr.2015.7298594},
}

@Article{Jang2018,
    author        = {Eric Jang and Coline Devin and Vincent Vanhoucke and Sergey Levine},
    title         = {Grasp2Vec: Learning Object Representations from Self-Supervised Grasping},
    journal       = {CoRR},
    year          = {2018},
    volume        = {abs/1811.06964},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1811-06964},
    eprint        = {1811.06964},
    timestamp     = {Sun, 25 Nov 2018 18:57:12 +0100},
    url           = {http://arxiv.org/abs/1811.06964},
}

@Article{LeNet,
    author    = {Y. Lecun and L. Bottou and Y. Bengio and P. Haffner},
    title     = {Gradient-based learning applied to document recognition},
    journal   = {Proceedings of the {IEEE}},
    year      = {1998},
    volume    = {86},
    number    = {11},
    pages     = {2278--2324},
    doi       = {10.1109/5.726791},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@InProceedings{Gu2017,
    author    = {Shixiang Gu and Ethan Holly and Timothy Lillicrap and Sergey Levine},
    title     = {Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates},
    booktitle = {2017 {IEEE} International Conference on Robotics and Automation ({ICRA})},
    year      = {2017},
    month     = {may},
    publisher = {{IEEE}},
    doi       = {10.1109/icra.2017.7989385},
}

@Article{DDPG,
    author        = {Timothy P. Lillicrap and Jonathan J. Hunt and Alexander Pritzel and Nicolas Heess and Tom Erez and Yuval Tassa and David Silver and Daan Wierstra},
    title         = {Continuous control with deep reinforcement learning},
    journal       = {CoRR},
    year          = {2015},
    volume        = {abs/1509.02971},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/LillicrapHPHETS15},
    eprint        = {1509.02971},
    timestamp     = {Mon, 13 Aug 2018 16:46:11 +0200},
    url           = {http://arxiv.org/abs/1509.02971},
}
@article{NAF,
abstract = {In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to learn, improve, adapt and reproduce tasks with dynamically changing constraints based on exploration and autonomous learning. We give a summary of the state-of-the-art of reinforcement learning in the context of robotics, in terms of both algorithms and policy representations. Numerous challenges faced by the policy representation in robotics are identified. Three recent examples for the application of reinforcement learning to real-world robots are described: a pancake flipping task, a bipedal walking energy minimization task and an archery-based aiming task. In all examples, a state-of-the-art expectation-maximization-based reinforcement learning is used, and different policy representations are proposed and evaluated for each task. The proposed policy representations offer viable solutions to six rarely-addressed challenges in policy representations: correlations, adaptability, multi-resolution, globality, multi-dimensionality and convergence. Both the successes and the practical difficulties encountered in these examples are discussed. Based on insights from these particular cases, conclusions are drawn about the state-of-the-art and the future perspective directions for reinforcement learning in robotics.},
archivePrefix = {arXiv},
arxivId = {1603.00748},
author = {Kormushev, Petar and Calinon, Sylvain and Caldwell, Darwin G.},
doi = {10.3390/robotics2030122},
eprint = {1603.00748},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Gu et al. - 2016 - Continuous deep q-learning with model-based acceleration(2).pdf:pdf;:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2016{\_}Continuous deep q-learning with model-based acceleration{\_}Gu et al.pdf:pdf},
isbn = {3405062780},
issn = {22186581},
journal = {Robotics},
keywords = {Learning and adaptive systems,Reinforcement learning,Robotics},
number = {3},
pages = {122--148},
pmid = {21487784},
title = {{Continuous Deep Q-Learning with Model-based Acceleration}},
volume = {2},
year = {2013}
}

@Article{QLearning,
    author  = {Christopher J. C. H. Watkins and Peter Dayan},
    title   = {Q-Learning},
    journal = {Machine Learning},
    year    = {1992},
    volume  = {8},
    number  = {3},
}

@inproceedings{VGGNet,
abstract = {In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3 × 3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.},
archivePrefix = {arXiv},
arxivId = {1409.1556},
author = {Simonyan, Karen and Zisserman, Andrew},
booktitle = {3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings},
eprint = {1409.1556},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Simonyan, Zisserman - 2015 - Very deep convolutional networks for large-scale image recognition.pdf:pdf},
publisher = {International Conference on Learning Representations, ICLR},
title = {{Very deep convolutional networks for large-scale image recognition}},
year = {2015}
}

@Article{DuelingNetwork,
    author        = {Ziyu Wang and Nando de Freitas and Marc Lanctot},
    title         = {Dueling Network Architectures for Deep Reinforcement Learning},
    journal       = {CoRR},
    year          = {2015},
    volume        = {abs/1511.06581},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/WangFL15},
    eprint        = {1511.06581},
    timestamp     = {Mon, 13 Aug 2018 16:48:17 +0200},
    url           = {http://arxiv.org/abs/1511.06581},
}

@Article{PrioritizedExperienceReplay,
    author        = {Tom Schaul and John Quan and Ioannis Antonoglou and David Silver},
    title         = {Prioritized Experience Replay},
    journal       = {CoRR},
    year          = {2015},
    volume        = {abs/1511.05952},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/SchaulQAS15},
    eprint        = {1511.05952},
    timestamp     = {Mon, 13 Aug 2018 16:46:28 +0200},
    url           = {http://arxiv.org/abs/1511.05952},
}
@article{Dmitry2018,
abstract = {In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execute the desired grasp, our method enables closed-loop vision-based control, whereby the robot continuously updates its grasp strategy based on the most recent observations to optimize long-horizon grasp success. To that end, we introduce QT-Opt, a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters to perform closed-loop, real-world grasping that generalizes to 96{\%} grasp success on unseen objects. Aside from attaining a very high success rate, our method exhibits behaviors that are quite distinct from more standard grasping systems: using only RGB vision-based perception from an over-the-shoulder camera, our method automatically learns regrasping strategies, probes objects to find the most effective grasps, learns to reposition objects and perform other non-prehensile pre-grasp manipulations, and responds dynamically to disturbances and perturbations.},
archivePrefix = {arXiv},
arxivId = {1806.10293},
author = {Kalashnikov, Dmitry and Irpan, Alex and Pastor, Peter and Ibarz, Julian and Herzog, Alexander and Jang, Eric and Quillen, Deirdre and Holly, Ethan and Kalakrishnan, Mrinal and Vanhoucke, Vincent and Levine, Sergey},
eprint = {1806.10293},
file = {:Users/atsushi/Google{\_}Drive{\_}a{\_}yamada0703/東大/paper/2018{\_}QT-Opt Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation{\_}Kalashnikov et al.pdf:pdf},
keywords = {deep learning,grasping,reinforcement learning},
number = {CoRL},
pages = {1--23},
title = {{QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation}},
url = {http://arxiv.org/abs/1806.10293},
year = {2018}
}

@Article{Dmitry2018,
    author        = {Dmitry Kalashnikov and Alex Irpan and Peter Pastor and Julian Ibarz and Alexander Herzog and Eric Jang and Deirdre Quillen and Ethan Holly and Mrinal Kalakrishnan and Vincent Vanhoucke and Sergey Levine},
    title         = {QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation},
    journal       = {CoRR},
    year          = {2018},
    volume        = {abs/1806.10293},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1806-10293},
    eprint        = {1806.10293},
    timestamp     = {Mon, 13 Aug 2018 16:48:22 +0200},
    url           = {http://arxiv.org/abs/1806.10293},
}

@Article{A3C,
    author        = {Volodymyr Mnih and Adri{\`{a}} Puigdom{\`{e}}nech Badia and Mehdi Mirza and Alex Graves and Timothy P. Lillicrap and Tim Harley and David Silver and Koray Kavukcuoglu},
    title         = {Asynchronous Methods for Deep Reinforcement Learning},
    journal       = {CoRR},
    year          = {2016},
    volume        = {abs/1602.01783},
    archiveprefix = {arXiv},
    bibsource     = {dblp computer science bibliography, https://dblp.org},
    biburl        = {https://dblp.org/rec/bib/journals/corr/MnihBMGLHSK16},
    eprint        = {1602.01783},
    timestamp     = {Mon, 13 Aug 2018 16:47:40 +0200},
    url           = {http://arxiv.org/abs/1602.01783},
}

@PhdThesis{ExperienceReplay,
    author    = {Lin, Long-Ji},
    title     = {Reinforcement Learning for Robots Using Neural Networks},
    school    = {Carnegie Mellon University Pittsburgh},
    year      = {1992},
    address   = {Pittsburgh, PA, USA},
    note      = {UMI Order No. GAX93-22750},
    publisher = {Carnegie Mellon University},
}

@InProceedings{FittedQ,
    author    = {Riedmiller, Martin},
    title     = {Neural Fitted Q Iteration -- First Experiences with a Data Efficient Neural Reinforcement Learning Method},
    booktitle = {Machine Learning: ECML 2005},
    year      = {2005},
    editor    = {Gama, Jo{\~a}o and Camacho, Rui and Brazdil, Pavel B. and Jorge, Al{\'i}pio M{\'a}rio and Torgo, Lu{\'i}s},
    pages     = {317--328},
    address   = {Berlin, Heidelberg},
    publisher = {Springer Berlin Heidelberg},
    abstract  = {This paper introduces NFQ, an algorithm for efficient and effective training of a Q-value function represented by a multi-layer perceptron. Based on the principle of storing and reusing transition experiences, a model-free, neural network based Reinforcement Learning algorithm is proposed. The method is evaluated on three benchmark problems. It is shown empirically, that reasonably few interactions with the plant are needed to generate control policies of high quality.},
    isbn      = {978-3-540-31692-3},
}

@article{simpleRNN,
    author    = {Yann LeCun and
    Yoshua Bengio and
    Geoffrey E. Hinton},
    title     = {Deep learning},
    journal   = {Nature},
    volume    = {521},
    number    = {7553},
    pages     = {436--444},
    year      = {2015},
    url       = {https://doi.org/10.1038/nature14539},
    doi       = {10.1038/nature14539},
    timestamp = {Wed, 14 Nov 2018 00:00:00 +0100},
    biburl    = {https://dblp.org/rec/bib/journals/nature/LeCunBH15},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Dropout,
    author  = {Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov},
    title   = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
    journal = {Journal of Machine Learning Research},
    year    = {2014},
    volume  = {15},
    pages   = {1929-1958},
    url     = {http://jmlr.org/papers/v15/srivastava14a.html}
}

@inproceedings{BatchNorm,
    author = {Ioffe, Sergey and Szegedy, Christian},
    title = {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
    booktitle = {Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37},
    series = {ICML'15},
    year = {2015},
    location = {Lille, France},
    pages = {448--456},
    numpages = {9},
    url = {http://dl.acm.org/citation.cfm?id=3045118.3045167},
    acmid = {3045167},
    publisher = {JMLR.org},
}

@inproceedings{Adam,
abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
archivePrefix = {arXiv},
arxivId = {1412.6980},
author = {Kingma, Diederik P. and Ba, Jimmy Lei},
booktitle = {3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings},
eprint = {1412.6980},
publisher = {International Conference on Learning Representations, ICLR},
title = {{Adam: A method for stochastic optimization}},
year = {2015}
}

@article{SIFT,
    author = {Lowe, David G.},
    title = {Distinctive Image Features from Scale-Invariant Keypoints},
    journal = {Int. J. Comput. Vision},
    issue_date = {November 2004},
    volume = {60},
    number = {2},
    month = nov,
    year = {2004},
    issn = {0920-5691},
    pages = {91--110},
    numpages = {20},
    url = {https://doi.org/10.1023/B:VISI.0000029664.99615.94},
    doi = {10.1023/B:VISI.0000029664.99615.94},
    acmid = {996342},
    publisher = {Kluwer Academic Publishers},
    address = {Hingham, MA, USA},
    keywords = {image matching, invariant features, object recognition, scale invariance},
}

@article{HOG,
    abstract = {We study the question of feature sets for robust visual object recognition,
    adopting linear SVM based human detection as a test case. After reviewing
    existing edge and gradient based descriptors, we show experimentally
    that grids of Histograms of Oriented Gradient (HOG) descriptors significantly
    outperform existing feature sets for human detection. We study the
    influence of each stage of the computation on performance, concluding
    that fine-scale gradients, fine orientation binning, relatively coarse
    spatial binning, and high-quality local contrast normalization in
    overlapping descriptor blocks are all important for good results.
    The new approach gives near-perfect separation on the original MIT
    pedestrian database, so we introduce a more challenging dataset containing
    over 1800 annotated human images with a large range of pose variations
    and backgrounds.},
    added-at = {2009-08-24T22:23:57.000+0200},
    author = {Dalal, N. and Triggs, B.},
    biburl = {https://www.bibsonomy.org/bibtex/23765f5c9ab9abdf0efe63d50c64147d5/sitrke},
    citeulike-article-id = {335784},
    citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467360},
    description = {Human Detection},
    file = {:paperpool\\hog.pdf:PDF},
    interhash = {a19602b4d8fd42e73d957ae456280c17},
    intrahash = {3765f5c9ab9abdf0efe63d50c64147d5},
    journal = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer
    Society Conference on},
    keywords = {detection, vision},
    pages = {886--893},
    posted-at = {2007-03-13 17:24:40},
    priority = {2},
    timestamp = {2009-08-24T22:23:57.000+0200},
    title = {Histograms of Oriented Gradients for Human Detection},
    url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467360},
    volume = {1},
    year = {2005}
}

@article{SURF,
abstract = {This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters. We conclude the article with SURF's application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF's usefulness in a broad range of topics in computer vision. {\textcopyright} 2007 Elsevier Inc. All rights reserved.},
author = {Bay, Herbert and Ess, Andreas and Tuytelaars, Tinne and {Van Gool}, Luc},
doi = {10.1016/j.cviu.2007.09.014},
file = {:Users/atsushi/Library/Application Support/Mendeley Desktop/Downloaded/Bay et al. - 2008 - Speeded-Up Robust Features (SURF).pdf:pdf},
issn = {10773142},
journal = {Computer Vision and Image Understanding},
keywords = {Camera calibration,Feature description,Interest points,Local features,Object recognition},
mendeley-groups = {ComputerVision},
month = {jun},
number = {3},
pages = {346--359},
title = {{Speeded-Up Robust Features (SURF)}},
volume = {110},
year = {2008}
}

@article{SVM,
    added-at = {2013-09-04T15:01:39.000+0200},
    author = {Cortes, C. and Vapnik, V.},
    biburl = {https://www.bibsonomy.org/bibtex/22b1eb8bea07ae0156a53a4e9c6eac1df/thoni},
    interhash = {c223c465141618ad63aac5a6132280f7},
    intrahash = {2b1eb8bea07ae0156a53a4e9c6eac1df},
    journal = {Machine Learning},
    keywords = {machine network support vector},
    pages = {273-297},
    timestamp = {2016-09-06T08:23:07.000+0200},
    title = {Support Vector Networks},
    volume = {20},
    year = {1995}
}

@article{M2model,
    author    = {Diederik P. Kingma and
    Danilo Jimenez Rezende and
    Shakir Mohamed and
    Max Welling},
    title     = {Semi-Supervised Learning with Deep Generative Models},
    journal   = {CoRR},
    volume    = {abs/1406.5298},
    year      = {2014},
    url       = {http://arxiv.org/abs/1406.5298},
    archivePrefix = {arXiv},
    eprint    = {1406.5298},
    timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},
    biburl    = {https://dblp.org/rec/bib/journals/corr/KingmaRMW14},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Backprop,
abstract = {We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal 'hidden' units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure 1. {\textcopyright} 1986 Nature Publishing Group.},
author = {Rumelhart, David E. and Hinton, Geoffrey E. and Williams, Ronald J.},
doi = {10.1038/323533a0},
issn = {00280836},
journal = {Nature},
number = {6088},
pages = {533--536},
title = {{Learning representations by back-propagating errors}},
volume = {323},
year = {1986}
}

@article{SSD,
    added-at = {2016-12-03T00:00:00.000+0100},
    author = {Liu, Wei and Anguelov, Dragomir and Erhan, Dumitru and Szegedy, Christian and Reed, Scott E. and Fu, Cheng-Yang and Berg, Alexander C.},
    biburl = {https://www.bibsonomy.org/bibtex/2ba015352ae6d8ef89e5885f38b30ff77/dblp},
    ee = {http://arxiv.org/abs/1512.02325},
    interhash = {3de0ab12eb5f85e1a99264c83b0d0016},
    intrahash = {ba015352ae6d8ef89e5885f38b30ff77},
    journal = {CoRR},
    keywords = {dblp},
    timestamp = {2016-12-06T11:34:26.000+0100},
    title = {SSD: Single Shot MultiBox Detector.},
    url = {http://dblp.uni-trier.de/db/journals/corr/corr1512.html#LiuAESR15},
    volume = {abs/1512.02325},
    year = 2015
}

@article{YOLOv3,
    added-at = {2018-08-13T00:00:00.000+0200},
    author = {Redmon, Joseph and Farhadi, Ali},
    biburl = {https://www.bibsonomy.org/bibtex/25c9942eff0dac7cd3f00ae3ec886a88e/dblp},
    ee = {http://arxiv.org/abs/1804.02767},
    interhash = {bbdec3df168e9809d9e61423d4b4e062},
    intrahash = {5c9942eff0dac7cd3f00ae3ec886a88e},
    journal = {CoRR},
    keywords = {dblp},
    timestamp = {2018-08-14T13:39:38.000+0200},
    title = {YOLOv3: An Incremental Improvement.},
    url = {http://dblp.uni-trier.de/db/journals/corr/corr1804.html#abs-1804-02767},
    volume = {abs/1804.02767},
    year = 2018
}

@article{Unet,
    added-at = {2018-08-13T00:00:00.000+0200},
    author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
    biburl = {https://www.bibsonomy.org/bibtex/2b99e0743410b0939acaeb871134a21d7/dblp},
    ee = {http://arxiv.org/abs/1505.04597},
    interhash = {9158de16b2caff7458df054dc6fc2748},
    intrahash = {b99e0743410b0939acaeb871134a21d7},
    journal = {CoRR},
    keywords = {dblp},
    timestamp = {2018-08-14T13:02:37.000+0200},
    title = {U-Net: Convolutional Networks for Biomedical Image Segmentation.},
    url = {http://dblp.uni-trier.de/db/journals/corr/corr1505.html#RonnebergerFB15},
    volume = {abs/1505.04597},
    year = 2015
}

@article{Hosseini-AslGE16,
    added-at = {2018-08-13T00:00:00.000+0200},
    author = {Hosseini-Asl, Ehsan and Gimel'farb, Georgy L. and El-Baz, Ayman},
    biburl = {https://www.bibsonomy.org/bibtex/24b90a9a904a43bae0bd537ba98b8eaaa/dblp},
    ee = {http://arxiv.org/abs/1607.00556},
    interhash = {3578cf6f80e08143bf808a5225b81885},
    intrahash = {4b90a9a904a43bae0bd537ba98b8eaaa},
    journal = {CoRR},
    keywords = {dblp},
    timestamp = {2018-08-14T12:14:52.000+0200},
    title = {Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network.},
    url = {http://dblp.uni-trier.de/db/journals/corr/corr1607.html#Hosseini-AslGE16},
    volume = {abs/1607.00556},
    year = 2016
}

@inproceedings{GAN,
    added-at = {2014-12-10T00:00:00.000+0100},
    author = {Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron C. and Bengio, Yoshua},
    biburl = {https://www.bibsonomy.org/bibtex/2c658b5ec42920435360d333978136d50/dblp},
    booktitle = {NIPS},

    editor = {Ghahramani, Zoubin and Welling, Max and Cortes, Corinna and Lawrence, Neil D. and Weinberger, Kilian Q.},
    ee = {http://papers.nips.cc/paper/5423-generative-adversarial-nets},
    interhash = {5e7e73013b6ed68fa953fdb6cb55f05f},
    intrahash = {c658b5ec42920435360d333978136d50},
    keywords = {dblp},
    pages = {2672-2680},
    timestamp = {2015-06-19T08:34:13.000+0200},
    title = {Generative Adversarial Nets.},
    url = {http://dblp.uni-trier.de/db/conf/nips/nips2014.html#GoodfellowPMXWOCB14},
    year = 2014
}

@inproceedings{xingjian2015convolutional,
    added-at = {2016-04-08T00:00:00.000+0200},
    author = {Shi, Xingjian and Chen, Zhourong and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-Kin and chun Woo, Wang},
    biburl = {https://www.bibsonomy.org/bibtex/251bdd06cb23e4ecfcd9c444fb67eb4b7/dblp},
    booktitle = {NIPS},

    editor = {Cortes, Corinna and Lawrence, Neil D. and Lee, Daniel D. and Sugiyama, Masashi and Garnett, Roman},
    ee = {http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting},
    interhash = {b6d0c65d2d62ed0824cd0bcc9ae5ddd4},
    intrahash = {51bdd06cb23e4ecfcd9c444fb67eb4b7},
    keywords = {dblp},
    pages = {802-810},
    timestamp = {2016-07-15T11:42:45.000+0200},
    title = {Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting.},
    url = {http://dblp.uni-trier.de/db/conf/nips/nips2015.html#ShiCWYWW15},
    year = 2015
}

@article{bao2017cvae,
    title={CVAE-GAN: fine-grained image generation through asymmetric training},
    author={Bao, Jianmin and Chen, Dong and Wen, Fang and Li, Houqiang and Hua, Gang},
    journal={CoRR, abs/1703.10155},
    volume={5},
    year={2017}
}

@article{dou20163d,
    author    = {Qi Dou and Hao Chen and Yueming Jin and Lequan Yu and Jing Qin and Pheng-Ann Heng},
    title     = {3D Deeply Supervised Network for Automatic Liver Segmentation from {CT} Volumes},
    booktitle = {Medical Image Computing and Computer-Assisted Intervention {\textendash} {MICCAI} 2016},
    publisher = {Springer International Publishing},
    year      = {2016},
    pages     = {149--157},
    doi       = {10.1007/978-3-319-46723-8_18}
}

@article{BroschTYLTT16,
    added-at = {2018-11-14T00:00:00.000+0100},
    author = {Brosch, Tom and Tang, Lisa Y. W. and Yoo, Youngjin and Li, David K. B. and Traboulsee, Anthony and Tam, Roger C.},
    biburl = {https://www.bibsonomy.org/bibtex/27c363ac59bbe0345474a863cd2aa826b/dblp},
    ee = {https://www.wikidata.org/entity/Q39990248},
    interhash = {b3ce65d9ca731fccb834d3626728f37e},
    intrahash = {7c363ac59bbe0345474a863cd2aa826b},
    journal = {IEEE Trans. Med. Imaging},
    keywords = {dblp},
    number = 5,
    pages = {1229-1239},
    timestamp = {2018-11-15T12:03:51.000+0100},
    title = {Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.},
    url = {http://dblp.uni-trier.de/db/journals/tmi/tmi35.html#BroschTYLTT16},
    volume = 35,
    year = 2016
}

@inproceedings{grad-cam,
    added-at = {2018-01-11T00:00:00.000+0100},
    author = {Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
    biburl = {https://www.bibsonomy.org/bibtex/2f510d2fbeb8f8bc21f44ff62df3d291a/dblp},
    booktitle = {ICCV},
    crossref = {conf/iccv/2017},
    ee = {http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.74},
    interhash = {14e7991f774cb73e9dd13d23fd8898ab},
    intrahash = {f510d2fbeb8f8bc21f44ff62df3d291a},
    isbn = {978-1-5386-1032-9},
    keywords = {dblp},
    pages = {618-626},
    publisher = {IEEE Computer Society},
    timestamp = {2018-01-13T11:40:20.000+0100},
    title = {Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization.},
    url = {http://dblp.uni-trier.de/db/conf/iccv/iccv2017.html#SelvarajuCDVPB17},
    year = 2017
}

@article{inglese2017deep,
    title={Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer},
    author={Inglese, Paolo and McKenzie, James S and Mroz, Anna and Kinross, James and Veselkov, Kirill and Holmes, Elaine and Takats, Zoltan and Nicholson, Jeremy K and Glen, Robert C},
    journal={Chemical science},
    volume={8},
    number={5},
    pages={3500--3511},
    year={2017},
    publisher={Royal Society of Chemistry}
}

@article{hoo2016deep,
    title={Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning},
    author={Hoo-Chang, Shin and Roth, Holger R and Gao, Mingchen and Lu, Le and Xu, Ziyue and Nogues, Isabella and Yao, Jianhua and Mollura, Daniel and Summers, Ronald M},
    journal={IEEE transactions on medical imaging},
    volume={35},
    number={5},
    pages={1285},
    year={2016},
    publisher={NIH Public Access}
}

@article{havaei2017brain,
    title={Brain tumor segmentation with deep neural networks},
    author={Havaei, Mohammad and Davy, Axel and Warde-Farley, David and Biard, Antoine and Courville, Aaron and Bengio, Yoshua and Pal, Chris and Jodoin, Pierre-Marc and Larochelle, Hugo},
    journal={Medical image analysis},
    volume={35},
    pages={18--31},
    year={2017},
    publisher={Elsevier}
}

@article{lecun2015deep,
    title={Deep learning},
    author={LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
    journal={nature},
    volume={521},
    number={7553},
    pages={436},
    year={2015},
    publisher={Nature Publishing Group}
}

@article{hoo2016deep,
    title={Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning},
    author={Hoo-Chang, Shin and Roth, Holger R and Gao, Mingchen and Lu, Le and Xu, Ziyue and Nogues, Isabella and Yao, Jianhua and Mollura, Daniel and Summers, Ronald M},
    journal={IEEE transactions on medical imaging},
    volume={35},
    number={5},
    pages={1285},
    year={2016},
    publisher={NIH Public Access}
}

@article{RadfordMC15,
    added-at = {2018-08-13T00:00:00.000+0200},
    author = {Radford, Alec and Metz, Luke and Chintala, Soumith},
    biburl = {https://www.bibsonomy.org/bibtex/2dd5fb901fbe12c53b342e5b77062460b/dblp},
    ee = {http://arxiv.org/abs/1511.06434},
    interhash = {ae6fc4b7593a1d0e31aeeff9fef81a36},
    intrahash = {dd5fb901fbe12c53b342e5b77062460b},
    journal = {CoRR},
    keywords = {dblp},
    timestamp = {2018-08-14T15:04:08.000+0200},
    title = {Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.},
    url = {http://dblp.uni-trier.de/db/journals/corr/corr1511.html#RadfordMC15},
    volume = {abs/1511.06434},
    year = {2015}
}

@inproceedings{COCO論文,
    abstract = {We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model. {\textcopyright} 2014 Springer International Publishing.},
    archivePrefix = {arXiv},
    arxivId = {1405.0312},
    author = {Lin, Tsung Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'{a}}r, Piotr and Zitnick, C. Lawrence},
    booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    doi = {10.1007/978-3-319-10602-1_48},
    eprint = {1405.0312},
    file = {::},
    issn = {16113349},
    mendeley-groups = {ComputerVision},
    number = {PART 5},
    pages = {740--755},
    publisher = {Springer Verlag},
    title = {{Microsoft COCO: Common objects in context}},
    volume = {8693 LNCS},
    year = {2014}
}

@inproceedings{HSR2018,
    author = {Yamamoto, Takashi and Terada, Koji and Ochiai, Akiyoshi and Saito, Fuminori and Asahara, Yoshiaki and Murase, Kazuto},
    year = {2018},
    month = {10},
    pages = {7675-7682},
    title = {Development of the Research Platform of a Domestic Mobile Manipulator Utilized for International Competition and Field Test},
    doi = {10.1109/IROS.2018.8593798}
}

@article {jamminggripper,
	author = {Brown, Eric and Rodenberg, Nicholas and Amend, John and Mozeika, Annan and Steltz, Erik and Zakin, Mitchell R. and Lipson, Hod and Jaeger, Heinrich M.},
	title = {Universal robotic gripper based on the jamming of granular material},
	volume = {107},
	number = {44},
	pages = {18809--18814},
	year = {2010},
	doi = {10.1073/pnas.1003250107},
	publisher = {National Academy of Sciences},
	abstract = {Gripping and holding of objects are key tasks for robotic manipulators. The development of universal grippers able to pick up unfamiliar objects of widely varying shape and surface properties remains, however, challenging. Most current designs are based on the multifingered hand, but this approach introduces hardware and software complexities. These include large numbers of controllable joints, the need for force sensing if objects are to be handled securely without crushing them, and the computational overhead to decide how much stress each finger should apply and where. Here we demonstrate a completely different approach to a universal gripper. Individual fingers are replaced by a single mass of granular material that, when pressed onto a target object, flows around it and conforms to its shape. Upon application of a vacuum the granular material contracts and hardens quickly to pinch and hold the object without requiring sensory feedback. We find that volume changes of less than 0.5\% suffice to grip objects reliably and hold them with forces exceeding many times their weight. We show that the operating principle is the ability of granular materials to transition between an unjammed, deformable state and a jammed state with solid-like rigidity. We delineate three separate mechanisms, friction, suction, and interlocking, that contribute to the gripping force. Using a simple model we relate each of them to the mechanical strength of the jammed state. This advance opens up new possibilities for the design of simple, yet highly adaptive systems that excel at fast gripping of complex objects.},
	issn = {0027-8424},
	URL = {https://www.pnas.org/content/107/44/18809},
	eprint = {https://www.pnas.org/content/107/44/18809.full.pdf},
	journal = {Proceedings of the National Academy of Sciences}
}

@book{われはロボット,
    title={I, Robot},
    author={Asimov, I.},
    lccn={68070665},
    series={Robot series

license_type: permissive

branch_name: refs/heads/master

extension: bib