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

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arxiv 2506.13498 v1 pith:7PFD32EW submitted 2025-06-16 cs.RO cs.HCcs.LGcs.SYeess.SY

Toshiaki Tsuji , Yasuhiro Kato , Gokhan Solak , Heng Zhang , Tadej Petrič , Francesco Nori , Arash Ajoudani This is my paper
classification cs.ROcs.HCcs.LGcs.SYeess.SY
keywords contact-richlearningtasksimitationcomplexdynamicsfoundationmanipulation
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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