Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance

Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance

Dynamic movement primitives (DMPs) are a robust framework for movement generation from demonstrations. This framework can be extended by adding a perturbing term to achieve obstacle avoidance without sacrificing stability. The additional term is usually constructed based on potential functions. Alth...

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Bibliographic Details
Main Authors:	Ang Li, Zhenze Liu, Wenrui Wang, Mingchao Zhu, Yanhui Li, Qi Huo, Ming Dai
Format:	Article
Language:	English
Published:	MDPI AG 2021-11-01
Series:	Applied Sciences
Subjects:	obstacle avoidance Dynamic Movement Primitives reinforcement learning PI2 (policy improvement with path integrals)
Online Access:	https://www.mdpi.com/2076-3417/11/23/11184

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