[2605.21688] Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control
[Submitted on 20 May 2026]
Title:Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control
View a PDF of the paper titled Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control, by Alessandro Amici and 3 other authors
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Abstract:Autonomous contact-based micromanipulation is challenging because surface and interfacial interactions at the microscale are difficult to model accurately, limiting the use of conventional model-based control and sim-to-real learning. We present a closed-loop sim-to-real reinforcement learning (RL) approach for microfiber shape control on a surface. The central idea is to train geometric shape regulation in a simplified frictionless simulator and rely on real-time visual feedback during deployment to iteratively correct the observed effects of unmodeled surface interactions. An RL policy trained entirely in simulation is transferred directly to a physical dual-gripper micromanipulation system operating at 40 Hz, without retraining or domain adaptation. Using silk microfibers as a testbed, the policy achieves a mean point-wise shape error of 270 $\pm$ 80 $\mu$m across twenty-four diverse initial configurations. Across nine specimens covering all combinations of three fiber diameters (50, 80, and 120 $\mu$m) and three manipulated lengths (10 mm, 15mm, and 20 mm), the same policy achieves sub-millimeter final shape error without any retraining or retuning. These results show that a policy learned in a simplified simulator can achieve repeatable real-world microfiber shape regulation under surface contact, provided that the task-relevant effects of the sim-to-real mismatch remain observable and correctable within the closed feedback loop.
Comments: 7 pages,7 figures
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2605.21688 [cs.RO]
(or arXiv:2605.21688v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.21688
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Houari Bettahar [view email] [v1] Wed, 20 May 2026 19:45:58 UTC (792 KB)
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