Fetching the paper…
Reading the bibliography…
Equivariant neural networks enforce symmetry within the structure of their convolutional layers, resulting in a substantial improvement in sample efficiency when learning an equivariant or invariant function.
Robust estimation of a location parameter
Peter J. Huber · 1964
Earlier work this paper cites.
Abstract algebra , volume 1999
David S Dummit and Richard M Foote · 1991
Earlier work this paper cites.
Symmetries and model minimization in markov decision processes, 2001
Balaraman Ravindran and Andrew G Barto · 2001
Earlier work this paper cites.
Plannable approximations to mdp homomorphisms: Equivariance under actions
Elise van der Pol, Thomas Kipf, Frans A Oliehoek, and Max Welling · 2002
Earlier work this paper cites.
Incorporating symmetry into deep dynamics models for improved generalization
Rui Wang, Robin Walters, and Rose Yu · 2002
Earlier work this paper cites.
Lie groups, Lie algebras, and representations: an elementary introduction , volume 10
Brian C Hall · 2003
Earlier work this paper cites.
Reinforcement learning with augmented data
Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas · 2004
Earlier work this paper cites.
Approximate homomorphisms: A framework for non-exact minimization in markov decision processes
Balaraman Ravindran and Andrew G Barto · 2004
Earlier work this paper cites.
Policy learning in se (3) action spaces
Dian Wang, Colin Kohler, and Robert Platt · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
Earlier work this paper cites.
Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2016
Earlier work this paper cites.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
A general theory of equivariant cnns on homogeneous spaces
Taco Cohen, Mario Geiger, and Maurice Weiler · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
Cited alongside, same era.
On-policy dataset synthesis for learning robot grasping policies using fully convolutional deep networks
Vishal Satish, Jeffrey Mahler, and Ken Goldberg · 2019
Later among the works it cites.
General e ( 2 ) e(2) -equivariant steerable cnns
Maurice Weiler and Gabriele Cesa · 2019
Later among the works it cites.
Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio G’omez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott E. Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, D. Budden, Mel Vecerík, Oleg O. Sushkov, David Barker, Jonathan Scholz, Misha Denil, N. D. Freitas, and Ziyun Wang · 2020
Later among the works it cites.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Ilya Kostrikov, Denis Yarats, and Rob Fergus · 2020
Later among the works it cites.
Invariant transform experience replay: Data augmentation for deep reinforcement learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods
Deirdre Quillen, Eric Jang, Ofir Nachum, Chelsea Finn, J. Ibarz, and Sergey Levine · 2018
Cited alongside, same era.
Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2018
Cited alongside, same era.
Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
Andy Zeng, Shuran Song, Kuan-Ting Yu, Elliott Donlon, Francois R Hogan, Maria Bauza, Daolin Ma, Orion Taylor, Melody Liu, Eudald Romo, et al · 2018
Cited alongside, same era.
Comparing task simplifications to learn closed-loop object picking using deep reinforcement learning
Michel Breyer, Fadri Furrer, Tonci Novkovic, R. Siegwart, and J. Nieto · 2019
Cited alongside, same era.
Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, A. Irpan, J. Ibarz, Sergey Levine, R. Hadsell, and Konstantinos Bousmalis · 2019
Cited alongside, same era.
Reinforcement learning for robotic manipulation using simulated locomotion demonstrations
Ozsel Kilinc, Yangfan Hu, and G. Montana · 2019
Cited alongside, same era.
Group equivariant convolutional networks
Taco Cohen and Max Welling
Cited in the paper.
Yijiong Lin, Jiancong Huang, Matthieu Zimmer, Yisheng Guan, Juan Rojas, and Paul Weng · 2020
Later among the works it cites.
Group equivariant deep reinforcement learning
Arnab Kumar Mondal, Pratheeksha Nair, and Kaleem Siddiqi · 2020
Later among the works it cites.
Learning visual servo policies via planner cloning
Ulrich Viereck, Kate Saenko, and Robert W. Platt · 2020
Later among the works it cites.
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
Later among the works it cites.
A framework for efficient robotic manipulation
Albert Zhan, Philip Zhao, Lerrel Pinto, Pieter Abbeel, and Michael Laskin · 2020
Later among the works it cites.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
Later among the works it cites.
Equivariant $q$ learning in spatial action spaces
Dian Wang, Robin Walters, Xupeng Zhu, and Robert Platt · 2021
Later among the works it cites.