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We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL) with a Transformer-based model that learns to combine proprioceptive information and high-dimensional depth sensor inputs.
A markovian decision process
Richard Bellman · 1957
Earlier work this paper cites.
Dynamic walk of a biped
Hirofumi Miura and Isao Shimoyama · 1984
Earlier work this paper cites.
Hopping in legged systems—modeling and simulation for the two-dimensional one-legged case
Marc H Raibert · 1984
Earlier work this paper cites.
Footprint–based quadruped motion synthesis
Nick Torkos and Michiel van de Panne · 1998
Earlier work this paper cites.
Architectures for a biomimetic hexapod robot
F. Delcomyn and M. Nelson · 2000
Earlier work this paper cites.
Positive force feedback in bouncing gaits?
Hartmut Geyer, Andre Seyfarth, and Reinhard Blickhan · 2003
Earlier work this paper cites.
Policy gradient reinforcement learning for fast quadrupedal locomotion
Nate Kohl and Peter Stone · 2004
Earlier work this paper cites.
Realization of a cnn-driven cockroach-inspired robot
P. Arena, L. Fortuna, M. Frasca, L. Patané, and M. Pavone · 2006
Earlier work this paper cites.
Simbicon: Simple biped locomotion control
KangKang Yin, Kevin Loken, and Michiel Van de Panne · 2007
Earlier work this paper cites.
Decoupling representation learning from reinforcement learning
Adam Stooke, Kimin Lee, Pieter Abbeel, and Michael Laskin · 2009
Earlier work this paper cites.
Dynamics randomization revisited: A case study for quadrupedal locomotion
Zhaoming Xie, Xingye Da, Michiel van de Panne, Buck Babich, and Animesh Garg · 2011
Earlier work this paper cites.
Control of dynamic gaits for a quadrupedal robot
Christian Gehring, Stelian Coros, Marco Hutter, Michael Blösch, Mark A. Hoepflinger, and Roland Siegwart · 2013
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Earlier work this paper cites.
Emergence of locomotion behaviours in rich environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin A. Riedmiller, and David Silver · 2017
Earlier work this paper cites.
Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, and Koray Kavukcuoglu · 2017
Earlier work this paper cites.
Learning to navigate in complex environments
P. Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andy Ballard, Andrea Banino, Misha Denil, R. Goroshin, L. Sifre, K. Kavukcuoglu, D. Kumaran, and R. Hadsell · 2017
Earlier work this paper cites.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
Earlier work this paper cites.
Deeploco: dynamic locomotion skills using hierarchical deep reinforcement learning
X. Peng, G. Berseth, KangKang Yin, and M. V. D. Panne · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Attention is all you need, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Mit cheetah 3: Design and control of a robust, dynamic quadruped robot
Gerardo Bledt, Matthew J Powell, Benjamin Katz, Jared Di Carlo, Patrick M Wensing, and Sangbae Kim · 2018
Earlier work this paper cites.
More than a feeling: Learning to grasp and regrasp using vision and touch
R. Calandra, Andrew Owens, Dinesh Jayaraman, Justin Lin, Wenzhen Yuan, Jitendra Malik, E. Adelson, and Sergey Levine · 2018
Earlier work this paper cites.
Dynamic locomotion in the MIT cheetah 3 through convex model-predictive control
Jared Di Carlo, Patrick M. Wensing, Benjamin Katz, Gerardo Bledt, and Sangbae Kim · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Dynamic locomotion in the mit cheetah 3 through convex model-predictive control
Jared Di Carlo, Patrick M Wensing, Benjamin Katz, Gerardo Bledt, and Sangbae Kim · 2018
Earlier work this paper cites.
Policies modulating trajectory generators
Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, and Vincent Vanhoucke · 2018
Earlier work this paper cites.
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2018
Earlier work this paper cites.
Gaze and the control of foot placement when walking in natural terrain
Jonathan Samir Matthis, Jacob L Yates, and Mary M Hayhoe · 2018
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Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
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Alexander Sax, Bradley Emi, Amir R. Zamir, Leonidas J. Guibas, Silvio Savarese, and Jitendra Malik · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
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Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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End-to-end object detection with transformers
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H. Zhang, S. Starke, T. Komura, and Jun Saito · 2018
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Zero-shot terrain generalization for visual locomotion policies, 2020
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Momentum contrast for unsupervised visual representation learning
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