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Robots must know how to be gentle when they need to interact with fragile objects, or when the robot itself is prone to wear and tear.
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Constrained Markov Decision Processes
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Mammalian play: Training for the unexpected
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Safety evaluation method of design and control for human-care robots
Koji Ikuta, Hideki Ishii, and Makoto Nokata · 2003
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Q-decomposition for reinforcement learning agents
Stuart Russell and Andrew L. Zimdars · 2003
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y. Ng · 2004
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Configuration control of space robots for impact minimization
Panfeng Huang, Wenfu Xu, Bin Liang, and Yangsheng Xu · 2006
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Understanding children’s injury-risk behaviors: The independent contributions of cognitions and emotions
Barbara A. Morrongiello and Shawn Matheis · 2007
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Categorising risky play—how can we identify risk-taking in children’s play?
Ellen B. H. Sandseter · 2007
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What is intrinsic motivation? A typology of computational approaches
Pierre-Yves Oudeyer and Frederic Kaplan · 2009
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Risky play and children’s safety: Balancing priorities for optimal child development
Mariana Brussoni, Lise L. Olsen, Ian Pike, and David A. Sleet · 2012
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Sensing tactile microvibrations with the biotac—comparison with human sensitivity
Jeremy A. Fishel and Gerald E. Loeb · 2012
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Learning skills from play: Artificial curiosity on a Katana robot arm
Hung Quoc Ngo, Matthew D. Luciw, Alexander Förster, and Jürgen Schmidhuber · 2012
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MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Playing Atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Intrinsically motivated learning of real-world sensorimotor skills with developmental constraints
Pierre-Yves Oudeyer, Adrien Baranes, and Frédéric Kaplan · 2013
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A survey of multi-objective sequential decision-making
Diederik M. Roijers, Peter Vamplew, Shimon Whiteson, and Richard Dazeley · 2013
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The effects of task difficulty, novelty and the size of the search space on intrinsically motivated exploration
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Continuous control with deep reinforcement learning
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Mastering the game of Go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016
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Multimodal imitation using self-learned sensorimotor representations
Martina Zambelli and Yiannis Demiris · 2016
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Surprise-based intrinsic motivation for deep reinforcement learning
Joshua Achiam and Shankar Sastry · 2017
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Adrien F Baranes, Pierre-Yves Oudeyer, and Jacqueline Gottlieb · 2014
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Minimizing energy consumption for robot arm movement
Abdullah Mohammed, Bernard Schmidt, Lihui Wang, and Liang Gao · 2014
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Safe exploration techniques for reinforcement learning – an overview
Martin Pecka and Tomas Svoboda · 2014
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Tactile object recognition using deep learning and dropout
Alexander Schmitz, Yusuke Bansho, Kuniaki Noda, Hiroyasu Iwata, Tetsuya Ogata, and Shigeki Sugano · 2014
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A comprehensive survey on safe reinforcement learning
Javier García and Fernando Fernández · 2015
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Multiobjective reinforcement learning: A comprehensive overview
Chunming Liu, Xin Xu, and Dewen Hu · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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EX2: exploration with exemplar models for deep reinforcement learning
Justin Fu, John D. Co-Reyes, and Sergey Levine · 2017
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Pre-impact configuration designing of a robot manipulator for impact minimization
Jingchen Hu and Tianshu Wang · 2017
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A tactile-based framework for active object learning and discrimination using multimodal robotic skin
Mohsen Kaboli, Di Feng, Kunpeng Yao, Pablo Lanillos, and Gordon Cheng · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
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#Exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 2017
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Learning awareness models
Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, and Misha Denil · 2018
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Distributed distributional deterministic policy gradients
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, and Timothy P. Lillicrap · 2018
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Safe exploration in continuous action spaces
Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik, Todd Hester, Cosmin Paduraru, and Yuval Tassa · 2018
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Intrinsically motivated reinforcement learning for human–robot interaction in the real-world
Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa, and Hiroshi Ishiguro · 2018
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Reward constrained policy optimization
Chen Tessler, Daniel J. Mankowitz, and Shie Mannor · 2018
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