{"ID":6537400,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11734","arxiv_id":"2607.11734","title":"NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception","abstract":"Differentiable simulators have advanced policy learning and model-based control, yet actuator dynamics remain an important source of sim-to-real error. This is particularly acute on low-cost platforms, where the linear current-to-torque relation $τ= K_tI$ becomes unreliable during commanded-target tracking because of friction, hysteresis, backlash, and thermal effects. We present NeuralActuator, a neural actuator model that jointly predicts (i) a simulator-equivalent generalized-effort surrogate for trajectory propagation on low-cost servo platforms, (ii) external force with a contact-probability gate for sensorless force perception, and (iii) a motor-condition score for the supervised joint. We also introduce the Neural Actuation Dataset (NAD), collected with a twin-arm teleoperation system that records robot states and actuator telemetry together with external-force labels. The torque-surrogate head is trained through differentiable simulation from pose trajectories without direct generalized-effort labels, while the force, gate, and motor-condition heads receive direct supervision. A Transformer captures temporal dependencies while supporting real-time inference. We evaluate NeuralActuator on a 5-DoF OpenManipulator-X, a 6-DoF SO-101, and a 7-DoF Franka Emika Panda, spanning three actuator families and platforms costing approximately USD 500 to over USD 30,000. The low-cost platforms support dynamics and force evaluation, while the offline Franka experiment provides an additional payload-force-estimation benchmark. Experiments further demonstrate its application for motor condition estimation on OpenManipulator-X and improved behavior-cloning performance when NeuralActuator is used as a pretrained module.","short_abstract":"Differentiable simulators have advanced policy learning and model-based control, yet actuator dynamics remain an important source of sim-to-real error. This is particularly acute on low-cost platforms, where the linear current-to-torque relation $τ= K_tI$ becomes unreliable during commanded-target tracking because of f...","url_abs":"https://arxiv.org/abs/2607.11734","url_pdf":"https://arxiv.org/pdf/2607.11734v1","authors":"[\"Zhiyang Dou\",\"John U. Onyemelukwe\",\"Hangxing Zhang\",\"Heng Zhang\",\"Minghao Guo\",\"Yunsheng Tian\",\"Michal Piotr Lipiec\",\"Joshua Jacob\",\"Chao Liu\",\"Peter Yichen Chen\",\"Yuri Ivanov\",\"Wojciech Matusik\"]","published":"2026-07-13T15:58:34Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\",\"cs.GR\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}