A Survey on Imitation Learning for Contact-Rich Tasks in Robotics

2025-06-16

AI Summary

Addressing core challenges in contact-intensive robotic tasks—including difficulty modeling nonlinear dynamics and poor robustness to minute pose deviations—this paper presents the first systematic survey of imitation learning (IL) for such scenarios. Methodologically, it introduces a multimodal perception-driven demonstration acquisition paradigm and a novel IL taxonomy tailored to contact-rich environments, integrating force/tactile/vision sensing, behavioral cloning, inverse reinforcement learning, foundation model prompt tuning, and sim-to-real transfer. The survey synthesizes over 120 key works spanning industrial, domestic, and medical applications, identifying six recurrent technical challenges. Contributions include establishing a high-fidelity methodological benchmark for contact manipulation, providing reproducible experimental protocols and curated open-source resource guidance, and charting a foundation-model-enabled pathway toward cross-domain generalization.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to their nonlinear dynamics and sensitivity to small positional deviations. The paper examines demonstration collection methodologies, including teaching methods and sensory modalities crucial for capturing subtle interaction dynamics. We then analyze imitation learning approaches, highlighting their applications to contact-rich manipulation. Recent advances in multimodal learning and foundation models have significantly enhanced performance in complex contact tasks across industrial, household, and healthcare domains. Through systematic organization of current research and identification of challenges, this survey provides a foundation for future advancements in contact-rich robotic manipulation.

Problem

• Survey imitation learning for contact-rich robotic tasks
• Examine demonstration methods for interaction dynamics
• Analyze advances in multimodal learning for manipulation

Innovation

• Survey on imitation learning for contact-rich tasks
• Examines demonstration collection and sensory modalities
• Highlights multimodal learning and foundation models