A Survey on Imitation Learning for Contact-Rich Tasks in Robotics
Journal:
arXiv
Published Date:
Jun 16, 2025
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.
