Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control

• URL: http://arxiv.org/abs/2605.21688v1
• Date: Wed, 20 May 2026 19:45:58 GMT
• Title: Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control
• Abstract summary: We present a closed-loop sim-to-real reinforcement learning approach for microfiber shape control on a surface.<n>We show that a policy learned in a simplified simulator can achieve repeatable real-world microfiber shape regulation under surface contact.
• Score: 41.91836259982757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $μ$m across twenty-four diverse initial configurations. Across nine specimens covering all combinations of three fiber diameters (50, 80, and 120 $μ$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.

Related papers

• Task-Relevant Feature-Dynamics Fidelity Enables Zero-Shot Sim-to-Real Transfer for Robotic Ultrasound Scanning [58.25007580703646]
  We develop a TR-oriented ultrasound simulator that combines a shared structural intermediate domain, trajectory-level fixed noise, and few-step conditional flow generation.<n>In phantom experiments, a policy trained exclusively in simulation succeeded in 390 of 400 zero-shot deployments across four target planes.
  arXiv  Detail & Related papers  (2026-08-30T02:37:00Z)
• TactSpace: Learning a Physics-enriched Shared Latent Space for Tactile Sim-to-Real Transfer [8.357674069719524]
  We propose a multi-modal representation learning framework that aligns heterogeneous tactile modalities within a shared latent space.<n>Our approach employs modality-specific encoders to project diverse tactile observations into a common embedding space.<n>Our results demonstrate zero-shot sim-to-real transfer across physically dissimilar representations.
  arXiv  Detail & Related papers  (2026-06-17T11:41:27Z)
• Real-World Deployment of Massively Parallel Sampling-Based MPC for Contact-Rich Manipulation [21.501604407170273]
  We present an MPC framework that leverages JAX for large-scale parallelization and efficient computation.<n>We deploy it on a Franka Research 3 executing the Push-T manipulation task through a complete real-to-sim-to-real pipeline.<n>These findings highlight key challenges for sampling-based MPC in contact-rich manipulation-contact sensitivity, tight compute budgets, and the difficulty of obtaining informative domain-randomization signals in real time.
  arXiv  Detail & Related papers  (2026-06-16T12:24:05Z)
• Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions [59.49967357689445]
  Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies.<n>This study presents a complete open-source dilated real-based framework for the development and validation of deep learning models for affective touch recognition in soft interactive companions.
  arXiv  Detail & Related papers  (2026-04-16T10:20:05Z)
• SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds [36.87909899472009]
  We introduce SIM1, a physics-aligned real-to-sim-to-real data engine that grounds simulation in the physical world.<n>The system digitizes scenes into metric-consistent twins, calibrates deformable dynamics through elastic modeling, and expands behaviors via diffusion-based trajectory generation.<n>Experiments show that policies trained on purely synthetic data achieve parity with real-data baselines at a 1:15 equivalence ratio, while delivering 90% zero-shot success and 50% generalization gains in real-world deployment.
  arXiv  Detail & Related papers  (2026-04-09T17:59:52Z)
• SOFTMAP: Sim2Real Soft Robot Forward Modeling via Topological Mesh Alignment and Physics Prior [21.30232105970865]
  SOFTMAP is a sim-real learning framework for real-time 3D forward modeling of tendon-actuated soft finger manipulators.<n>We evaluate SOFTMAP on both simulated and physical hardware, achieving shape prediction accuracy with a Chamfer distance of 0.389 mm in simulation.<n>Our results show that SOFTMAP provides a data-efficient approach for 3D forward modeling and control of soft manipulators.
  arXiv  Detail & Related papers  (2026-03-19T18:21:44Z)
• CLASH: Collision Learning via Augmented Sim-to-real Hybridization to Bridge the Reality Gap [9.148510140521706]
  We introduce Collision Learning via Augmented Sim-to-real Hybridization (CLASH), a data-efficient framework that learns a parameter-conditioned impulsive collision surrogate model.<n>We demonstrate that policies obtained with our hybrid simulator transfer more robustly to the real world.
  arXiv  Detail & Related papers  (2026-02-21T03:39:33Z)
• Simulating Environments with Reasoning Models for Agent Training [55.98861707136674]
  Building bespoke environments for training is heavy, brittle, and limits progress.<n>We propose two frameworks: Simia-SFT and Simia-RL.<n>Simia-SFT and Simia-RL enable scalable agent training without environment engineering.
  arXiv  Detail & Related papers  (2025-11-03T18:29:57Z)
• World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks [53.98633183204453]
  In this paper, a novel world model-based learning framework is proposed to minimize packet-completeness-aware age of information (CAoI) in a vehicular network.<n>A world model framework is proposed to jointly learn a dynamic model of the mmWave V2X environment and use it to imagine trajectories for learning how to perform link scheduling.<n>In particular, the long-term policy is learned in differentiable imagined trajectories instead of environment interactions.
  arXiv  Detail & Related papers  (2025-05-03T06:23:18Z)
• PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation [4.875369637162596]
  PolyFit is a supervised learning framework designed for 5-DoF peg-in-hole assembly. It utilizes F/T data for accurate extrinsic pose estimation and adjusts the peg pose to rectify misalignments. It achieves impressive peg-in-hole success rates of 97.3% and 96.3% for seen and unseen shapes in simulations.
  arXiv  Detail & Related papers  (2023-12-05T06:28:33Z)
• Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing [42.27572349747162]
  We present a learning-based adaptive control strategy for aerial sliding tasks. The proposed controller structure combines data-driven and model-based control methods. Compared to fine-tuned state of the art interaction control methods we achieve reduced tracking error and improved disturbance rejection.
  arXiv  Detail & Related papers  (2022-06-28T16:28:59Z)
• Reinforcement Learning for Adaptive Mesh Refinement [63.7867809197671]
  We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning to train refinement policies directly from simulation. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations.
  arXiv  Detail & Related papers  (2021-03-01T22:55:48Z)
• Learning from Simulation, Racing in Reality [126.56346065780895]
  We present a reinforcement learning-based solution to autonomously race on a miniature race car platform. We show that a policy that is trained purely in simulation can be successfully transferred to the real robotic setup.
  arXiv  Detail & Related papers  (2020-11-26T14:58:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.