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arXiv · 2509.17053

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·Guyue Zhou
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Abstract

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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Haizhou Ge, Yufei Jia, Zheng Li, Yue Li, Zhixing Chen, Lu Shi, Lei Han, Ruqi Huang, Guyue Zhou. 2025-09-21. FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks. https://arxiv.org/abs/2509.17053

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