Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview
arxiv(2024)
摘要
Imitation Learning (IL), also referred to as Learning from Demonstration
(LfD), holds significant promise for capturing expert motor skills through
efficient imitation, facilitating adept navigation of complex scenarios. A
persistent challenge in IL lies in extending generalization from historical
demonstrations, enabling the acquisition of new skills without re-teaching.
Dynamical system-based IL (DSIL) emerges as a significant subset of IL
methodologies, offering the ability to learn trajectories via movement
primitives and policy learning based on experiential abstraction. This paper
emphasizes the fusion of theoretical paradigms, integrating control theory
principles inherent in dynamical systems into IL. This integration notably
enhances robustness, adaptability, and convergence in the face of novel
scenarios. This survey aims to present a comprehensive overview of DSIL
methods, spanning from classical approaches to recent advanced approaches. We
categorize DSIL into autonomous dynamical systems and non-autonomous dynamical
systems, surveying traditional IL methods with low-dimensional input and
advanced deep IL methods with high-dimensional input. Additionally, we present
and analyze three main stability methods for IL: Lyapunov stability,
contraction theory, and diffeomorphism mapping. Our exploration also extends to
popular policy improvement methods for DSIL, encompassing reinforcement
learning, deep reinforcement learning, and evolutionary strategies.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要