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Imitation Learning is a promising paradigm for learning complex robot manipulation skills by reproducing behavior from human demonstrations.
“Scaling data-driven robotics with reward sketching and batch reinforcement learning”
Serkan Cabi et al · 1909
Earlier work this paper cites.
“A unified approach for motion and force control of robot manipulators: The operational space formulation”
Oussama Khatib · 1987
Earlier work this paper cites.
“Alvinn: An autonomous land vehicle in a neural network”
Dean Pomerleau · 1989
Earlier work this paper cites.
“Using expectation-maximization for reinforcement learning”
Peter Dayan and Geoffrey Hinton · 1997
Earlier work this paper cites.
“Apprenticeship learning via inverse reinforcement learning”
Pieter Abbeel and Andrew Ng · 2004
Earlier work this paper cites.
“Reinforcement learning by reward-weighted regression for operational space control”
Jan Peters and Stefan Schaal · 2007
Earlier work this paper cites.
“Interactive policy learning through confidence-based autonomy”
Sonia Chernova and Manuela Veloso · 2009
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“Fitted Q-iteration by advantage weighted regression”
Gerhard Neumann and Jan Peters · 2009
Earlier work this paper cites.
“Inverse reinforcement learning”
Pieter Abbeel and Andrew Ng · 2011
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.
“Algorithmic and human teaching of sequential decision tasks”
Maya Cakmak and Manuel Lopes · 2012
Earlier work this paper cites.
“Mujoco: A physics engine for model-based control”
Emanuel Todorov, Tom Erez and Yuval Tassa · 2012
Earlier work this paper cites.
“robosuite: A Modular Simulation Framework and Benchmark for Robot Learning”
Yuke Zhu, Josiah Wong, Ajay Mandlekar and Roberto Martín-Martín · 2012
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
“Cooperative inverse reinforcement learning”
Dylan Hadfield-Menell, Stuart Russell, Pieter Abbeel and Anca Dragan · 2016
Cited alongside, same era.
“Generative adversarial imitation learning”
Jonathan Ho and Stefano Ermon · 2016
Cited alongside, same era.
“Showing versus doing: Teaching by demonstration”
Mark Ho et al · 2016
Cited alongside, same era.
“Shiv: Reducing supervisor burden in dagger using support vectors for efficient learning from demonstrations in high dimensional state spaces”
Michael Laskey et al · 2016
Cited alongside, same era.
“Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning”
Robert Loftin et al · 2016
Cited alongside, same era.
“Learning robot objectives from physical human interaction”
Andrea Bajcsy, Dylan Losey, Marcia O’Malley and Anca Dragan · 2017
“RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation”
Ajay Mandlekar et al · 2018
Later among the works it cites.
“Reinforcement and imitation learning for diverse visuomotor skills”
Yuke Zhu et al · 2018
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“Machine teaching for inverse reinforcement learning: Algorithms and applications”
Daniel Brown and Scott Niekum · 2019
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“HG-dagger: Interactive imitation learning with human experts”
Michael Kelly, Chelsea Sidrane, Katherine Driggs-Campbell and Mykel Kochenderfer · 2019
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Ajay Mandlekar et al · 2019
Later among the works it cites.
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“Deep reinforcement learning from human preferences”
Paul Christiano et al · 2017
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“Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations”
Michael Laskey et al · 2017
Cited alongside, same era.
“Dart: Noise injection for robust imitation learning”
Michael Laskey et al · 2017
Cited alongside, same era.
“Interactive learning from policy-dependent human feedback”
James MacGlashan et al · 2017
Cited alongside, same era.
“Policies for active learning from demonstration”
Brandon Packard and Santiago Ontanón · 2017
Cited alongside, same era.
“Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation”
Tianhao Zhang et al · 2017
Cited alongside, same era.
Xue Peng, Aviral Kumar, Grace Zhang and Sergey Levine · 2019
Later among the works it cites.
“Learning human objectives by evaluating hypothetical behavior”
Siddharth Reddy et al · 2019
Later among the works it cites.
“End-to-end robotic reinforcement learning without reward engineering”
Avi Singh et al · 2019
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“Positive-unlabeled reward learning”
Danfei Xu and Misha Denil · 2019
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“Leveraging human guidance for deep reinforcement learning tasks”
Ruohan Zhang et al · 2019
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Trevor Ablett, Filip Marić and Jonathan Kelly · 2020
Closest in time.
“Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation”
Joseph DelPreto et al · 2020
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“Pragmatic-pedagogic value alignment”
Jaime Fisac et al · 2020
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“Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations”
Ajay Mandlekar et al · 2020
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