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Many Imitation and Reinforcement Learning approaches rely on the availability of expert-generated demonstrations for learning policies or value functions from data.
Chomp: Gradient optimization techniques for efficient motion planning
Nathan Ratliff, Matt Zucker, J Andrew Bagnell, and Siddhartha Srinivasa · 2009
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Approximate inference and stochastic optimal control
Konrad Rawlik, Marc Toussaint, and Sethu Vijayakumar · 2010
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STOMP: Stochastic trajectory optimization for motion planning
M. Kalakrishnan, S. Chitta, E. Theodorou, P. Pastor, and S. Schaal · 2011
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Relative entropy and free energy dualities: Connections to path integral and kl control
Evangelos A Theodorou and Emanuel Todorov · 2012
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Chomp: Covariant hamiltonian optimization for motion planning
Matt Zucker, Nathan Ratliff, Anca D Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher M Dellin, J Andrew Bagnell, and Siddhartha S Srinivasa · 2013
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Batch continuous-time trajectory estimation as exactly sparse gaussian process regression
Tim D Barfoot, Chi Hay Tong, and Simo Särkkä · 2014
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Motion planning with sequential convex optimization and convex collision checking
John Schulman, Yan Duan, Jonathan Ho, Alex Lee, Ibrahim Awwal, Henry Bradlow, Jia Pan, Sachin Patil, Ken Goldberg, and Pieter Abbeel · 2014
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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Gaussian process motion planning
Mustafa Mukadam, Xinyan Yan, and Byron Boots · 2016
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
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Simultaneous trajectory estimation and planning via probabilistic inference
Mustafa Mukadam, Jing Dong, Frank Dellaert, and Byron Boots · 2017
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A stein variational newton method
Gianluca Detommaso, Tiangang Cui, Alessio Spantini, Youssef Marzouk, and Robert Scheichl · 2018
Cited alongside, same era.
Reinforcement learning and control as probabilistic inference: Tutorial and review
Stein point markov chain monte carlo
Wilson Ye Chen, Alessandro Barp, Franccois-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey, and Chris. J. Oates · 2019
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Non-smooth newton methods for deformable multi-body dynamics
Miles Macklin, Kenny Erleben, Matthias Müller, Nuttapong Chentanez, Stefan Jeschke, and Viktor Makoviychuk · 2019
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Stein variational gradient descent with matrix-valued kernels
Dilin Wang, Ziyang Tang, Chandrajit Bajaj, and Qiang Liu · 2019
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Learning dexterous manipulation from suboptimal experts
Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, and Francesco Nori · 2020
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Local search for policy iteration in continuous control
Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, et al · 2020
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Sergey Levine · 2018
Cited alongside, same era.
Continuous-time Gaussian process motion planning via probabilistic inference
Mustafa Mukadam, Jing Dong, Xinyan Yan, Frank Dellaert, and Byron Boots · 2018
Cited alongside, same era.
Stein variational model predictive control
Alexander Lambert, Adam Fishman, Dieter Fox, Byron Boots, and Fabio Ramos
Cited in the paper.
Dual online stein variational inference for control and dynamics
Lucas Barcelos, Alexander Lambert, Rafael Oliveira, Paulo Borges, Byron Boots, and Fabio Ramos · 2021
Closest in time.
Imitation learning as f-divergence minimization
Liyiming Ke, Sanjiban Choudhury, Matt Barnes, Wen Sun, Gilwoo Lee, and Siddhartha Srinivasa · 2021
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