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Imitation learning techniques have been shown to be highly effective in real-world control scenarios, such as robotics.
“The Method of Probits”
C.. Bliss · 1934
Earlier work this paper cites.
“ALVINN: An Autonomous Land Vehicle in a Neural Network”
Dean. Pomerleau · 1988
Earlier work this paper cites.
“Theory and evaluation of human robot interactions”
Jean Scholtz · 2003
Earlier work this paper cites.
“Common metrics for human-robot interaction”
Aaron Steinfeld et al · 2006
Earlier work this paper cites.
“Maximum entropy inverse reinforcement learning”
Brian. Ziebart, Andrew Maas, J. Bagnell and Anind. Dey · 2008
Earlier work this paper cites.
“A survey of robot learning from demonstration”
Brenna. Argall, Sonia Chernova, Manuela Veloso and Brett Browning · 2009
Earlier work this paper cites.
“Efficient Reductions for Imitation Learning”
Stephane Ross and Drew Bagnell · 2010
Earlier work this paper cites.
“Autonomous Helicopter Aerobatics through Apprenticeship Learning”
Pieter Abbeel, Adam Coates and Andrew. Ng · 2010
Earlier work this paper cites.
“A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning”
Stephane Ross, Geoffrey Gordon and Drew Bagnell · 2011
Earlier work this paper cites.
“A policy-blending formalism for shared control”
Anca Dragan and Siddhartha Srinivasa · 2013
Earlier work this paper cites.
“Shared autonomy via hindsight optimization”
Shervin Javdani, Siddhartha Srinivasa and J Bagnell · 2015
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.
“Generative Adversarial Imitation Learning”
Jonathan Ho and Stefano Ermon · 2016
Earlier work this paper cites.
“Mastering the game of go without human knowledge”
David Silver et al · 2017
Earlier work this paper cites.
“Dart: Noise injection for robust imitation learning”
Michael Laskey et al · 2017
Earlier work this paper cites.
“Scalable deep reinforcement learning for vision-based robotic manipulation”
Dmitry Kalashnikov et al · 2018
Earlier work this paper cites.
“Deep q-learning from demonstrations”
Todd Hester et al · 2018
Cited alongside, same era.
“Overcoming exploration in reinforcement learning with demonstrations”
Ashvin Nair et al · 2018
Cited alongside, same era.
“Planning with trust for human-robot collaboration”
Min Chen et al · 2018
Cited alongside, same era.
“Trial without Error: Towards Safe Reinforcement Learning via Human Intervention”
William Saunders, Girish Sastry, Andreas Stuhlmüller and Owain Evans · 2018
Cited alongside, same era.
“HG-DAgger: Interactive Imitation Learning with Human Experts”
Michael Kelly, Chelsea Sidrane, Katherine Driggs-Campbell and Mykel. Kochenderfer · 2019
Cited alongside, same era.
“EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning”
Kunal Menda, Katherine Driggs-Campbell and Mykel. Kochenderfer · 2019
Cited alongside, same era.
“Efficient learning of safe driving policy via human-ai copilot optimization”
Quanyi Li, Zhenghao Peng and Bolei Zhou · 2022
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“The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models”
Cassidy Laidlaw and Anca Dragan · 2022
Later among the works it cites.
“Fleet-dagger: Interactive robot fleet learning with scalable human supervision”
Ryan Hoque et al · 2023
Later among the works it cites.
“Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment”
Huihan Liu et al · 2023
Later among the works it cites.
“Active Reward Learning from Online Preferences”
Vivek Myers, Erdem Biyik and Dorsa Sadigh · 2023
Later among the works it cites.
“Efficient online reinforcement learning with offline data”
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“Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning”
Tianhe Yu et al · 2019
Cited alongside, same era.
“Human-in-the-Loop Imitation Learning using Remote Teleoperation”
Ajay Mandlekar et al · 2020
Cited alongside, same era.
“Learning from interventions: Human-robot interaction as both explicit and implicit feedback”
Jonathan Spencer et al · 2020
Cited alongside, same era.
“When Humans Aren’t Optimal: Robots that Collaborate with Risk-Aware Humans”
Minae Kwon et al · 2020
Cited alongside, same era.
“Correct Me if I am Wrong: Interactive Learning for Robotic Manipulation”
Eugenio Chisari et al · 2021
Cited alongside, same era.
“Policy finetuning: Bridging sample-efficient offline and online reinforcement learning”
Tengyang Xie et al · 2021
Cited alongside, same era.
Philip Ball, Laura Smith, Ilya Kostrikov and Sergey Levine · 2023
Later among the works it cites.
“RLIF: Interactive Imitation Learning as Reinforcement Learning”
Jianlan Luo et al · 2023
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“A review of mathematical models of human trust in automation”
Lucero Rodriguez et al · 2023
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“Gymnasium”
Mark Towers et al · 2023
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“RoboCLIP: One Demonstration is Enough to Learn Robot Policies”
Sumedh Sontakke et al · 2023
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“TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction”
Yunfan Jiang et al · 2024
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“IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning”
Ryan Hoque et al · 2024
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“Trajectory Improvement and Reward Learning from Comparative Language Feedback”
Zhaojing Yang et al · 2024
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“Yell at your robot: Improving on-the-fly from language corrections”
Lucy Shi et al · 2024
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“Human-Interactive Robot Learning: Definition, Challenges, and Recommendations”, 2025
Kim Baraka et al · 2025
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“Effects of Robot Competency and Motion Legibility on Human Correction Feedback”
Shuangge Wang et al · 2025
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