Fetching the paper…
Reading the bibliography…
We study how the choice of visual perspective affects learning and generalization in the context of physical manipulation from raw sensor observations.
Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra · 1998
Earlier work this paper cites.
Eye-in-hand/eye-to-hand cooperation for visual servoing
Grégory Flandin, François Chaumette, and Eric Marchand · 2000
Earlier work this paper cites.
Eye-in-hand/eye-to-hand multi-camera visual servoing
Vincenzo Lippiello, Bruno Siciliano, and Luigi Villani · 2005
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
Earlier work this paper cites.
In search of the real inductive bias: On the role of implicit regularization in deep learning
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2014
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
Hands, dexterity, and the brain
Helge Ritter and Robert Haschke · 2015
Earlier work this paper cites.
Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
Earlier work this paper cites.
Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
Earlier work this paper cites.
Deep spatial autoencoders for visuomotor learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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.
One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
The effectiveness of data augmentation in image classification using deep learning
Luis Perez and Jason Wang · 2017
Earlier work this paper cites.
Gelsight: High-resolution robot tactile sensors for estimating geometry and force
Wenzhen Yuan, Siyuan Dong, and Edward H Adelson · 2017
Earlier work this paper cites.
Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 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.
Time-agnostic prediction: Predicting predictable video frames
Dinesh Jayaraman, Frederik Ebert, Alexei A Efros, 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.
Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations
Shuran Song, Andy Zeng, Johnny Lee, and Thomas Funkhouser · 2020
Later among the works it cites.
Positive-unlabeled reward learning
Danfei Xu and Misha Denil · 2020
Later among the works it cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
Later among the works it cites.
Active perception and representation for robotic manipulation
Youssef Zaky, Gaurav Paruthi, Bryan Tripp, and James Bergstra · 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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, and Jonathan Tompson · 2018
Cited alongside, same era.
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Universal planning networks: Learning generalizable representations for visuomotor control
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
Cited alongside, same era.
Reinforcement and imitation learning for diverse visuomotor skills
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, et al · 2018
Cited alongside, same era.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
Cited alongside, same era.
Infobot: Transfer and exploration via the information bottleneck
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Yoshua Bengio, and Sergey Levine · 2019
Cited alongside, same era.
Environmental drivers of systematicity and generalization in a situated agent
Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L McClelland, and Adam Santoro · 2019
Cited alongside, same era.
Generalization in reinforcement learning with selective noise injection and information bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, and Katja Hofmann · 2019
Cited alongside, same era.
Meld: Meta-reinforcement learning from images via latent state models
Tony Z Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn, and Sergey Levine · 2020
Later among the works it cites.
Task-relevant adversarial imitation learning
Konrad Zolna, Scott Reed, Alexander Novikov, Sergio Gomez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, and Ziyu Wang · 2020
Later among the works it cites.
Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2021
Later among the works it cites.
A workflow for offline model-free robotic reinforcement learning
Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, and Sergey Levine · 2021
Later among the works it cites.
Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Wenzhao Lian, Chang Su, Mel Vecerik, Ning Ye, Stefan Schaal, and Jon Scholz · 2021
Later among the works it cites.
What matters in learning from offline human demonstrations for robot manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2021
Later among the works it cites.
Offline reinforcement learning from images with latent space models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, and Chelsea Finn · 2021
Later among the works it cites.
Habitat 2.0: Training home assistants to rearrange their habitat
Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr Maksymets, et al · 2021
Later among the works it cites.
Coarse-to-fine for sim-to-real: Sub-millimetre precision across the workspace
Eugene Valassakis, Norman Di Palo, and Edward Johns · 2021
Later among the works it cites.
Example-driven model-based reinforcement learning for solving long-horizon visuomotor tasks
Bohan Wu, Suraj Nair, Li Fei-Fei, and Chelsea Finn · 2021
Later among the works it cites.
Mastering visual continuous control: Improved data-augmented reinforcement learning
Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto · 2021
Later among the works it cites.
Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
Later among the works it cites.
Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation
Rishabh Jangir, Nicklas Hansen, Sambaran Ghosal, Mohit Jain, and Xiaolong Wang · 2022
Closest in time.