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

2025-08-24

AI Summary

This paper addresses core challenges in robot manipulation (RM) under real-world conditions—namely, poor generalization of imitation learning (IL), deep modality coupling, and inconsistent evaluation criteria. To tackle these, we propose the first hierarchical IL taxonomy specifically designed for RM, systematically analyzing 127 key papers published between 2015 and 2023 and constructing a comprehensive technological evolution timeline. Our methodology integrates bibliometric analysis, cross-modal qualitative synthesis, and quantitative evaluation on unified benchmarks (e.g., RLBench, Libero), dissecting methods along three axes: input modalities, prior embedding strategies, and architectural design. We introduce the first standardized comparison across 14 comparable performance metrics, uncovering five critical open problems—including visual–actuation misalignment and limited few-shot generalization. Finally, we identify promising future directions, such as embodied prior modeling and multi-task continual imitation learning.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Surveying imitation learning methods for robotic manipulation
• Analyzing influential studies based on impact and quality
• Providing comparative evaluation of existing benchmark results

Innovation

• Imitation learning from human demonstrations
• Hierarchical classification and input analysis
• Benchmark evaluations and quantitative comparisons