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A Survey on Imitation Learning for Contact-Rich Tasks in Robotics
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A Survey on Imitation Learning for Contact-Rich Tasks in Robotics
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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.
Forward citations
Cited by 6 Pith papers
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CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts
Exact symplectic learning extends to open robotic systems via an algebraic structured canonical lift, improving OOD autoregressive prediction on pendulum, quadrotor, and quadruped with parameter-efficient SympNets.
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Spacetime Optimal-Transport Attention for Visuo-Haptic Imitation Learning of Contact-Rich Manipulation
SO-TA replaces standard attention with optimal-transport alignment across vision, force/torque, and proprioception to improve diffusion-policy performance on real-robot insertion and wiping tasks.
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Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware
A simple bilateral teleoperation law that clamps the leader's control input to the follower's input achieves both easy free motion and stable contact on low-cost hardware.
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CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts
Lifting non-conservative, actuated, and contact-constrained robot dynamics into an exactly symplectic phase-space map yields state-of-the-art out-of-distribution autoregressive rollout error at low parameter and FLOP cost.
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PRIME: Physically-consistent Robotic Inertial and Motion Estimation for Legged and Humanoid Robots
PRIME is a MAP optimization framework that refines onboard kinematics into dynamically consistent trajectories for legged robots while jointly estimating contact forces and inertial parameters using differentiable smo...
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CONTACT: CONtact-aware TACTile Learning for Robotic Disassembly
In contact-rich robotic disassembly, compact force-field tactile representations (TacFF) outperform vision-only and high-resolution tactile-image policies, especially in tight-tolerance and deformable tasks; naive fus...
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