{"ID":2868141,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17053","arxiv_id":"2509.17053","title":"FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks","abstract":"Contact-rich manipulation is crucial for robots to perform tasks requiring precise force control, such as insertion, assembly, and in-hand manipulation. 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. We also design complementary force feedback frameworks via handheld haptics and VR visualization to improve demonstration quality. Experiments show that FILIC significantly outperforms vision-only and joint-torque-based methods, achieving safer, more compliant, and adaptable contact-rich manipulation. Our code can be found in https://github.com/TATP-233/FILIC.","short_abstract":"Contact-rich manipulation is crucial for robots to perform tasks requiring precise force control, such as insertion, assembly, and in-hand manipulation. However, most imitation learning (IL) policies remain position-centric and lack explicit force awareness, and adding force/torque sensors to collaborative robot arms i...","url_abs":"https://arxiv.org/abs/2509.17053","url_pdf":"https://arxiv.org/pdf/2509.17053v1","authors":"[\"Haizhou Ge\",\"Yufei Jia\",\"Zheng Li\",\"Yue Li\",\"Zhixing Chen\",\"Ruqi Huang\",\"Guyue Zhou\"]","published":"2025-09-21T12:17:20Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":609549,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2868141,"paper_url":"https://arxiv.org/abs/2509.17053","paper_title":"FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks","repo_url":"https://github.com/TATP-233/FILIC","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}