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Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade.
Pyrobot: An open-source robotics framework for research and benchmarking
Murali, A., Chen, T., Alwala, K. V., Gandhi, D., Pinto, L., Gupta, S., and Gupta, A · 1906
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Impedance control: An approach to manipulation
Hogan, N · 1984
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Deep blue
Campbell, M., Hoane Jr, A. J., and Hsu, F.-h · 2002
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Robot competitions-ideal benchmarks for robotics research
Behnke, S · 2006
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A versatile generalized inverted kinematics implementation for collaborative working humanoid robots: The stack of tasks
Mansard, N., Stasse, O., Evrard, P., and Kheddar, A · 2009
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The sl simulation and real-time control software package
Schaal, S · 2009
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The highly adaptive sdm hand: Design and performance evaluation
Dollar, A. M. and Howe, R. D · 2010
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Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
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Competitions for Benchmarking: Task and Functionality Scoring Complete Performance Assessment
Amigoni, F., Bastianelli, E., Berghofer, J., Bonarini, A., Fontana, G., Hochgeschwender, N., Iocchi, L., Kraetzschmar, G., Lima, P., Matteucci, M., Miraldo, P., Nardi, D., and Schiaffonati, V · 2015
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Toward Replicable and Measurable Robotics Research [From the Guest Editors]
Bonsignorio, F. and del Pobil, A. P · 2015
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2015
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Design and fabrication of a soft robotic hand with embedded actuators and sensors
She, Y., Li, C., Cleary, J., and Su, H.-J · 2015
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
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A lightweight robotic arm with pneumatic muscles for robot learning
Büchler, D., Ott, H., and Peters, J · 2016
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Pybullet, a python module for physics simulation for games, robotics and machine learning
Coumans, E. and Bai, Y · 2016
Cited alongside, same era.
Benchmarking deep reinforcement learning for continuous control
Duan, Y., Chen, X., Houthooft, R., Schulman, J., and Abbeel, P · 2016
Cited alongside, same era.
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Pinto, L. and Gupta, A · 2016
Cited alongside, same era.
Design of a highly biomimetic anthropomorphic robotic hand towards artificial limb regeneration
Xu, Z. and Todorov, E · 2016
Cited alongside, same era.
Extending the openai gym for robotics: a toolkit for reinforcement learning using ros and gazebo
The Control Toolbox - an open-source C++ library for robotics, optimal and model predictive control, May 2018
Giftthaler, M., Neunert, M., Stäuble, M., and Buchli, J · 2018
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Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D · 2018
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Stable baselines
Hill, A., Raffin, A., Ernestus, M., Gleave, A., Kanervisto, A., Traore, R., Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Sidor, S., and Wu, Y · 2018
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., and Quillen, D · 2018
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Zamora, I., Lopez, N. G., Vilches, V. M., and Cordero, A. H · 2016
Cited alongside, same era.
ros_control: A generic and simple control framework for ros
Chitta, S., Marder-Eppstein, E., Meeussen, W., Pradeep, V., Rodríguez Tsouroukdissian, A., Bohren, J., Coleman, D., Magyar, B., Raiola, G., Lüdtke, M., and Fernández Perdomo, E · 2017
Cited alongside, same era.
Openai baselines
Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Sidor, S., Wu, Y., and Zhokhov, P · 2017
Cited alongside, same era.
Emergence of Locomotion Behaviours in Rich Environments
Heess, N., Dhruva, T. B., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Ali Eslami, S. M., Riedmiller, M., and Silver, D · 2017
Cited alongside, same era.
A mathematical introduction to robotic manipulation
Murray, R. M · 2017
Cited alongside, same era.
The robotarium: A remotely accessible swarm robotics research testbed
Pickem, D., Glotfelter, P., Wang, L., Mote, M., Ames, A., Feron, E., and Egerstedt, M · 2017
Cited alongside, same era.
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
Popov, I., Heess, N., Lillicrap, T., Hafner, R., Barth-Maron, G., Vecerik, M., Lampe, T., Tassa, Y., Erez, T., and Riedmiller, M · 2017
Cited alongside, same era.
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
Lowrey, K., Kolev, S., Dao, J., Rajeswaran, A., and Todorov, E · 2018
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Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. d. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al · 2018
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Robel: Robotics benchmarks for learning with low-cost robots
Ahn, M., Zhu, H., Hartikainen, K., Ponte, H., Gupta, A., Levine, S., and Kumar, V · 2019
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Garage: A toolkit for reproducible reinforcement learning research
garage contributors, T · 2019
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
James, S., Wohlhart, P., Kalakrishnan, M., Kalashnikov, D., Irpan, A., Ibarz, J., Levine, S., Hadsell, R., and Bousmalis, K · 2019
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Real-time reinforcement learning
Ramstedt, S. and Pal, C · 2019
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Replab: A reproducible low-cost arm benchmark platform for robotic learning
Yang, B., Zhang, J., Pong, V., Levine, S., and Jayaraman, D · 2019
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Learning dexterous in-hand manipulation
Andrychowicz, O. M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., et al · 2020
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An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
Grimminger, F., Meduri, A., Khadiv, M., Viereck, J., Wüthrich, M., Naveau, M., Berenz, V., Heim, S., Widmaier, F., Fiene, J., Badri-Spröwitz, A., and Righetti, L · 2020
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The ingredients of real world robotic reinforcement learning
Zhu, H., Yu, J., Gupta, A., Shah, D., Hartikainen, K., Singh, A., Kumar, V., and Levine, S · 2020
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