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We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods in continuous robotics domains.
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Probabilistic 3D multilabel real-time mapping for multi-object manipulation
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Van Hoof, and David Meger · 2018
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Learning 6-dof grasping and pick-place using attention focus
Marcus Gualtieri and Robert Platt · 2018
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Composable deep reinforcement learning for robotic manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, and Sergey Levine · 2018
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Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
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Improving sample efficiency in model-free reinforcement learning from images
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, and Rob Fergus · 2019
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Learning one-shot imitation from humans without humans
Alessandro Bonardi, Stephen James, and Andrew J Davison · 2020
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Volumetric grasping network: Real-time 6 dof grasp detection in clutter
Michel Breyer, Jen Jen Chung, Lionel Ott, Roland Siegwart, and Juan Nieto · 2020
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Learning manipulation skills via hierarchical spatial attention
Marcus Gualtieri and Robert Platt · 2020
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RLBench: The robot learning benchmark & learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J. Davison · 2020
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Stephen James, Michael Bloesch, and Andrew J Davison · 2018
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Sim-to-real reinforcement learning for deformable object manipulation
Jan Matas, Stephen James, and Andrew J Davison · 2018
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Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach
Douglas Morrison, Peter Corke, and Jürgen Leitner · 2018
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Asymmetric actor critic for image-based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2018
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
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Discriminator-actor-critic: Addressing sample inefficiency and reward bias in adversarial imitation learning
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Ilya Kostrikov, Denis Yarats, and Rob Fergus · 2020
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Object finding in cluttered scenes using interactive perception
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Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations
Shuran Song, Andy Zeng, Johnny Lee, and Thomas Funkhouser · 2020
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MoreFusion: Multi-object reasoning for 6D pose estimation from volumetric fusion
Kentaro Wada, Edgar Sucar, Stephen James, Daniel Lenton, and Andrew J Davison · 2020
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Transporter networks: Rearranging the visual world for robotic manipulation
Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, et al · 2020
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MT-Opt: Continuous multi-task robotic reinforcement learning at scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
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Q-attention: Enabling efficient learning for vision-based robotic manipulation
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