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FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks

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arxiv 2509.17053 v2 pith:ZVKMIXZS submitted 2025-09-21 cs.RO

FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks

Haizhou Ge , Yufei Jia , Zheng Li , Yue Li , Zhixing Chen , Lu Shi , Lei Han , Ruqi Huang
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Guyue Zhou
This is my paper
classification cs.RO
keywords forcetorquefilicmanipulationcontact-richimitationimpedancelearning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Many contact-rich manipulation tasks require precise force regulation. However, most imitation learning (IL) policies remain position-centric and lack explicit force awareness, and adding force/torque sensors to collaborative robot arms is often costly and requires additional hardware design. To overcome these issues, we propose FILIC, a Force-guided Imitation Learning framework with impedance torque control. FILIC integrates a Transformer-based IL policy with an impedance controller in a dual-loop structure, enabling compliant force-informed, force-executed manipulation. For robots without force/torque sensors, we introduce a cost-effective end-effector force estimator using joint torque measurements through analytical Jacobian-based inversion while compensating with model-predicted torques from a digital twin. Experiments show that FILIC significantly outperforms vision-only and joint-torque-based methods, achieving safer, more compliant, and adaptable contact-rich manipulation. The source code is available at https://github.com/OpenGHz/mujoco-wrench-estimator.git.

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Cited by 5 Pith papers

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  1. Phase-Conditioned Imitation Learning with Autonomous Failure Recovery for Robust Deformable Object Manipulation

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    A FiLM-conditioned ACT policy with multi-modal phase prediction raises dual-arm T-shirt hanging success from 56% to 87% via autonomous failure recovery.

  2. Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control

    cs.RO 2026-03 conditional novelty 6.0

    TER-DAgger improves robotic precision insertion success rates by over 37% via residual policies from edited trajectories and force-aware intervention triggers.

  3. FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning

    cs.RO 2026-06 unverdicted novelty 5.0

    NEXT estimates external torques from short free-motion data without hardware sensors and FIRST improves imitation learning by upsampling contact phases, yielding over 17% better task progress on long-horizon manipulat...

  4. Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control

    cs.RO 2026-03 conditional novelty 5.0

    TER-DAgger uses force-prediction mismatches to trigger human corrections and residual-policy training, lifting precision-insertion success from 40.0% to 77.2% on average.

  5. Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures

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