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We propose a method for sim-to-real robot learning which exploits simulator state information in a way that scales to many objects.
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Asymmetric actor critic for image-based robot learning
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Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation
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Self-supervised visual planning with temporal skip connections
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Se3-nets: Learning rigid body motion using deep neural networks
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Domain randomization for transferring deep neural networks from simulation to the real world
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Attention Is All You Need
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A simple neural network module for relational reasoning
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Robustness via retrying: Closed-loop robotic manipulation with self-supervised learning
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Using simulation and domain adaptation to improve efficiency of deep robotic grasping
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Relational deep reinforcement learning
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Curiosity-driven Exploration by Self-supervised Prediction
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell · 2017
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Hindsight experience replay
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. P. Abbeel, and W. Zaremba · 2017
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Visual reinforcement learning with imagined goals
A. V. Nair, V. Pong, M. Dalal, S. Bahl, S. Lin, and S. Levine · 2018
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Learning dexterous in-hand manipulation
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Sim-to-real transfer of robotic control with dynamics randomization
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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Learning ambidextrous robot grasping policies
J. Mahler, M. Matl, V. Satish, M. Danielczuk, B. DeRose, S. McKinley, and K. Goldberg · 2019
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Iterative reinforcement learning based design of dynamic locomotion skills for cassie
Z. Xie, P. Clary, J. Dao, P. Morais, J. Hurst, and M. van de Panne · 2019
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Learning agile and dynamic motor skills for legged robots
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter · 2019
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Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. D. Ratliff, and D. Fox · 2019
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Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks
M. A. Lee, Y. Zhu, K. Srinivasan, P. Shah, S. Savarese, L. Fei-Fei, A. Garg, and J. Bohg · 2019
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Learning Latent Space Dynamics for Tactile Servoing
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Deep reinforcement learning with relational inductive biases
V. Zambaldi, D. Raposo, A. Santoro, V. Bapst, Y. Li, I. Babuschkin, K. Tuyls, D. Reichert, T. Lillicrap, E. Lockhart, M. Shanahan, V. Langston, R. Pascanu, M. Botvinick, O. Vinyals, and P. Battaglia · 2019
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Combining physical simulators and object-based networks for control
A. Ajay, M. Bauza, J. Wu, N. Fazeli, J. B. Tenenbaum, A. Rodriguez, and L. P. Kaelbling · 2019
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Y. Zhang, J. Hare, and A. Prügel-Bennett · 2019
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