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Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive results in recent years.
“Asynchronous methods for deep reinforcement learning”
Volodymyr Mnih et al · 1937
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Neville Hogan · 1985
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“A unified approach for motion and force control of robot manipulators: The operational space formulation”
Oussama Khatib · 1987
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“Inertial Properties in Robotic Manipulation: An Object-Level Framework”
O. Khatib · 1995
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“Specification of force-controlled actions in the "task frame formalism"-a synthesis”
H. Bruyninckx and J. De Schutter · 1996
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“Movement imitation with nonlinear dynamical systems in humanoid robots”
A.. Ijspeert, J. Nakanishi and S. Schaal · 2002
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“Computational approaches to motor learning by imitation”
Stefan Schaal, Auke Ijspeert and Aude Billard · 2003
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“Compliant motion programming: The task frame formalism revisited”
Torsten Kr\"oger, Bernd Finkemeyer, Ulrike Thomas and Friedrich Wahl · 2004
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“A Probabilistic Programming by Demonstration Framework Handling Skill Constraints in Joint Space and Task Space”
S. Calinon and A. Billard · 2008
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“Cartesian impedance control of redundant and flexible-joint robots”
Christian Ott · 2008
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“A Survey of Robot Learning from Demonstration”
Brenna. Argall, Sonia Chernova, Manuela Veloso and Brett Browning · 2009
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“Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies”
S. Calinon, I. Sardellitti and D.. Caldwell · 2010
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“Impedance learning for robotic contact tasks using natural actor-critic algorithm”
Byungchan Kim, Jooyoung Park, Shinsuk Park and Sungchul Kang · 2010
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“A generalized path integral control approach to reinforcement learning”
Evangelos Theodorou, Jonas Buchli and Stefan Schaal · 2010
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“Learning variable impedance control”
Jonas Buchli, Freek Stulp, Evangelos Theodorou and Stefan Schaal · 2011
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“Learning force control policies for compliant manipulation”
M. Kalakrishnan, L. Righetti, P. Pastor and S. Schaal · 2011
Cited alongside, same era.
“Learning impedance control of antagonistic systems based on stochastic optimization principles”
Djordje Mitrovic, Stefan Klanke and Sethu Vijayakumar · 2011
Cited alongside, same era.
“Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors”
Auke Ijspeert et al · 2013
Cited alongside, same era.
“Optimal distribution of contact forces with inverse-dynamics control”
Ludovic Righetti et al · 2013
Cited alongside, same era.
“Learned graphical models for probabilistic planning provide a new class of movement primitives”
Elmar R\"uckert, Gerhard Neumann, Marc Toussaint and Wolfgang Maass · 2013
Cited alongside, same era.
“Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction”
“Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates”
Shixiang Gu, Ethan Holly, Timothy Lillicrap and Sergey Levine · 2017
Later among the works it cites.
“ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems”
James Harrison* et al · 2017
Later among the works it cites.
“Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?”
Xue Peng and Michiel van Panne · 2017
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“Proximal policy optimization algorithms”
John Schulman et al · 2017
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“Multilateral surgical pattern cutting in 2d orthotropic gauze with deep reinforcement learning policies for tensioning”
Brijen Thananjeyan et al · 2017
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“Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards”
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K. Kronander and A. Billard · 2014
Cited alongside, same era.
“Learning object-level impedance control for robust grasping and dexterous manipulation”
M. Li, H. Yin, K. Tahara and A. Billard · 2014
Cited alongside, same era.
“Towards learning hierarchical skills for multi-phase manipulation tasks”
Oliver Kroemer et al · 2015
Cited alongside, same era.
“Learning force-based manipulation of deformable objects from multiple demonstrations”
Alex Lee et al · 2015
Cited alongside, same era.
“Continuous control with deep reinforcement learning”
Timothy Lillicrap et al · 2015
Cited alongside, same era.
“Trust Region Policy Optimization.”
John Schulman et al · 2015
Cited alongside, same era.
“Adaptive human-inspired compliant contact primitives to perform surface–surface contact under uncertainty”
Mohammad Khansari, Ellen Klingbeil and Oussama Khatib · 2016
Cited alongside, same era.
Matej Vecer\’k et al · 2017
Later among the works it cites.
“Force-based variable impedance learning for robotic manipulation”
Fares. Abu-Dakka, Leonel Rozo and Darwin. Caldwell · 2018
Later among the works it cites.
“Soft Actor-Critic Algorithms and Applications”
Tuomas Haarnoja et al · 2018
Later among the works it cites.
“Real-time Perception meets Reactive Motion Generation”
Daniel Kappler et al · 2018
Later among the works it cites.
“Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification”
Yanan Li et al · 2018
Later among the works it cites.
“Learning motions from demonstrations and rewards with time-invariant dynamical systems based policies”, 2018
Joel Rey et al · 2018
Later among the works it cites.
“Reinforcement learning: An introduction”
Richard Sutton and Andrew Barto · 2018
Later among the works it cites.
“Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning”
Marc Toussaint, Kelsey Allen, Kevin Smith and Josh Tenenbaum · 2018
Later among the works it cites.
“Learning a Structured Neural Network Policy for a Hopping Task”
Julian Viereck, Jules Kozolinsky, Alexander Herzog and Ludovic Righetti · 2018
Later among the works it cites.
“Reinforcement and imitation learning for diverse visuomotor skills”
Yuke Zhu et al · 2018
Later among the works it cites.
“Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks”
Michelle Lee et al · 2019
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