Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges

TL;DR

This paper provides a comprehensive survey of imitation learning methods in robotic manipulation, analyzing influential studies, benchmarks, and challenges to guide future research in autonomous robot skill acquisition.

Contribution

It offers a structured taxonomy, chronological evolution, benchmark analysis, and identifies key challenges in applying imitation learning to robotic manipulation.

Findings

Key technological advancements over time

Benchmark results and quantitative evaluations

Identification of current challenges and future directions

Abstract

Robotic Manipulation (RM) is central to the advancement of autonomous robots, enabling them to interact with and manipulate objects in real-world environments. This survey focuses on RM methodologies that leverage imitation learning, a powerful technique that allows robots to learn complex manipulation skills by mimicking human demonstrations. We identify and analyze the most influential studies in this domain, selected based on community impact and intrinsic quality. For each paper, we provide a structured summary, covering the research purpose, technical implementation, hierarchical classification, input formats, key priors, strengths and limitations, and citation metrics. Additionally, we trace the chronological development of imitation learning techniques within RM policy (RMP), offering a timeline of key technological advancements. Where available, we report benchmark results and perform quantitative evaluations to compare existing methods. By synthesizing these insights, this review provides a comprehensive resource for researchers and practitioners, highlighting both the state of the art and the challenges that lie ahead in the field of robotic manipulation through imitation learning.