A Survey on Imitation Learning for Contact-Rich Tasks in Robotics
It addresses the challenge of enabling robots to perform contact-rich tasks, which is crucial for applications in industrial, household, and healthcare settings, but is incremental as it synthesizes existing research.
This survey examines imitation learning for contact-rich robotic tasks, which involve complex physical interactions, by analyzing demonstration collection methods and learning approaches, highlighting recent advances that have improved performance across various domains.
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.