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We present a method for enabling Reinforcement Learning of motor control policies for complex skills such as dexterous manipulation.
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
L E Kavraki, P Svestka, J-C Latombe, and M H Overmars · 1996
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Reorienting objects with a robot hand using grasp gaits
Susanna Leveroni and Kenneth Salisbury · 1996
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Rapidly-exploring random trees : a new tool for path planning
S LaValle · 1998
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Analysis of probabilistic roadmaps for path planning
L E Kavraki, M N Kolountzakis, and J-C Latombe · 1998
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Dextrous manipulation by rolling and finger gaiting
L Han and J C Trinkle · 1998
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Randomized manipulation planning for a multi-fingered hand by switching contact modes
M Yashima, Y Shiina, and H Yamaguchi · 2003
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Finger gaits planning for multifingered manipulation
Jijie Xu, T John Koo, and Zexiang Li · 2007
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Optimal kinodynamic motion planning using incremental sampling-based methods
Sertac Karaman and Emilio Frazzoli · 2010
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On dexterity and dexterous manipulation
Raymond R Ma and Aaron M Dollar · 2011
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Kinodynamic RRT*: Asymptotically optimal motion planning for robots with linear dynamics
Dustin J Webb and Jur van den Berg · 2013
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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Rearrangement planning using object-centric and robot-centric action spaces
Jennifer E King, Marco Cognetti, and Siddhartha S Srinivasa · 2016
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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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Parameter space noise for exploration
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, and Marcin Andrychowicz · 2017
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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2017
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Asymmetric actor critic for image based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Soft Actor-Critic: Off-Policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Exploring restart distributions
Arash Tavakoli, Vitaly Levdik, Riashat Islam, Christopher M Smith, and Petar Kormushev · 2018
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Sample-Efficient learning of nonprehensile manipulation policies via Physics-Based informed state distributions
Lerrel Pinto, Aditya Mandalika, Brian Hou, and Siddhartha Srinivasa · 2018
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Solving rubik’s cube with a robot hand
OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, and Lei Zhang · 2019
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Go-Explore: a new approach for Hard-Exploration problems
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2019
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Transferring dexterous manipulation from GPU simulation to a remote Real-World TriFinger
Arthur Allshire, Mayank Mittal, Varun Lodaya, Viktor Makoviychuk, Denys Makoviichuk, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Ankur Handa, and Animesh Garg · 2021
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Isaac gym: High performance GPU-Based physics simulation for robot learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State · 2021
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Model predictive Actor-Critic: Accelerating robot skill acquisition with deep reinforcement learning
Andrew S Morgan, Daljeet Nandha, Georgia Chalvatzaki, Carlo D’Eramo, Aaron M Dollar, and Jan Peters · 2021
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Legged locomotion in challenging terrains using egocentric vision
Ananye Agarwal, Ashish Kumar, Jitendra Malik, and Deepak Pathak · 2022
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In-Hand object rotation via rapid motor adaptation
Haozhi Qi, Ashish Kumar, Roberto Calandra, Yi Ma, and Jitendra Malik · 2022
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RL-RRT: Kinodynamic motion planning via learning reachability estimators from RL policies
Hao-Tien Lewis Chiang, Jasmine Hsu, Marek Fiser, Lydia Tapia, and Aleksandra Faust · 2019
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Harnessing reinforcement learning for neural motion planning
Tom Jurgenson and Aviv Tamar · 2019
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Optimality and approximation with policy gradient methods in markov decision processes
Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan · 2020
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Long-Range indoor navigation with PRM-RL
Anthony Francis, Aleksandra Faust, Hao-Tien Lewis Chiang, Jasmine Hsu, J Chase Kew, Marek Fiser, and Tsang-Wei Edward Lee · 2020
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Reinforcement learning with probabilistically complete exploration
Philippe Morere, Gilad Francis, Tom Blau, and Fabio Ramos · 2020
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Learning a decentralized multi-arm motion planner
Huy Ha, Jingxi Xu, and Shuran Song · 2020
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A sensorized multicurved robot finger with Data-Driven touch sensing via overlapping light signals
Pedro Piacenza, Keith Behrman, Benedikt Schifferer, Ioannis Kymissis, and Matei Ciocarlie · 2020
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Learning-guided exploration for efficient sampling-based motion planning in high dimensions
Liam Schramm and Abdeslam Boularias · 2022
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Complex in-hand manipulation via compliance-enabled finger gaiting and multi-modal planning
Andrew S Morgan, Kaiyu Hang, Bowen Wen, Kostas Bekris, and Aaron M Dollar · 2022
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Surprisingly robust In-Hand manipulation: An empirical study
Aditya Bhatt, Adrian Sieler, Steffen Puhlmann, and Oliver Brock · 2022
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On the feasibility of learning finger-gaiting in-hand manipulation with intrinsic sensing
Gagan Khandate, Maximilian Haas-Heger, and Matei Ciocarlie · 2022
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Visual dexterity: In-hand dexterous manipulation from depth
Tao Chen, Megha Tippur, Siyang Wu, Vikash Kumar, Edward Adelson, and Pulkit Agrawal · 2022
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Learning purely tactile In-Hand manipulation with a Torque-Controlled hand
Leon Sievers, Johannes Pitz, and Berthold Bäuml · 2022
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MoDem: Accelerating visual Model-Based reinforcement learning with demonstrations
Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, and Aravind Rajeswaran · 2022
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Robot parkour learning
Ziwen Zhuang, Zipeng Fu, Jianren Wang, Christopher Atkeson, Soeren Schwertfeger, Chelsea Finn, and Hang Zhao · 2023
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Sampling-based exploration for reinforcement learning of dexterous manipulation
Gagan Khandate, Siqi Shang, Eric T Chang, Tristan Luca Saidi, Yang Liu, Seth Matthew Dennis, Johnson Adams, and Matei Ciocarlie · 2023
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Dextrous tactile In-Hand manipulation using a modular reinforcement learning architecture
Johannes Pitz, Lennart Röstel, Leon Sievers, and Berthold Bäuml · 2023
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Imitation bootstrapped reinforcement learning
Hengyuan Hu, Suvir Mirchandani, and Dorsa Sadigh · 2023
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