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We propose a Model Predictive Control (MPC) method for collision-free navigation that uses amortized variational inference to approximate the distribution of optimal control sequences by training a normalizing flow conditioned on the start, goal and environment.
A new approach to linear filtering and prediction problems
R. E. Kalman · 1960
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Relative entropy and free energy dualities: Connections to Path Integral and KL control
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Danilo Rezende and Shakir Mohamed · 2015
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Variational inference: A review for statisticians
David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe · 2017
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Alexander Lambert, Adam Fishman, Dieter Fox, Byron Boots, and Fabio Ramos · 2020
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Variational Inference MPC for Bayesian Model-based Reinforcement Learning
Masashi Okada and Tadahiro Taniguchi · 2020
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Sample-efficient cross-entropy method for real-time planning
Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Jan Achterhold, Joerg Stueckler, Michal Rolinek, and Georg Martius · 2020
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Joe Watson, Hany Abdulsamad, and Jan Peters · 2020
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Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Zhisheng Xiao, Qing Yan, and Yali Amit · 2020
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Dual Online Stein Variational Inference for Control and Dynamics
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Durk P Kingma and Prafulla Dhariwal · 2018
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Ahmed H. Qureshi and Michael C. Yip · 2018
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Clark Zhang, Jinwook Huh, and Daniel D. Lee · 2018
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Lucas Barcelos, Alexander Lambert, Rafael Oliveira, Paulo Borges, Byron Boots, and Fabio Ramos · 2021
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Mpc-mpnet: Model-predictive motion planning networks for fast, near-optimal planning under kinodynamic constraints, 2021
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Variational Inference MPC using Tsallis Divergence
Ziyi Wang, Oswin So, Jason Gibson, Bogdan Vlahov, Manan Gandhi, Guan-Horng Liu, and Evangelos Theodorou · 2021
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Constrained stochastic optimal control with learned importance sampling: A path integral approach
Jan Carius, René Ranftl, Farbod Farshidian, and Marco Hutter · 2022
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