发表机构
MIT; Amazon（麻省理工学院; 亚马逊）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对低成本平台执行器动力学致模拟到现实误差问题，提出NeuralActuator模型，能联合预测多种内容，引入NAD数据集，通过可微模拟训练，经多平台实验验证其在电机状态估计等方面有应用价值。
AI 中文摘要
可微模拟器推动了策略学习和基于模型的控制，但执行器动力学仍是模拟到现实误差的重要来源，在低成本平台上尤其严重。本文提出神经执行器模型NeuralActuator，它能联合预测模拟器等效广义力替代物、外力及电机状态分数。还介绍了神经驱动数据集NAD。通过可微模拟训练扭矩替代头，其他头接受直接监督，Transformer捕捉时间依赖性并支持实时推理。在多个平台上评估，实验证明了其在电机状态估计等方面的应用。
英文摘要
Differentiable simulators have advanced policy learning and model-based control across robotic tasks. Yet actuator dynamics remain underexplored and can be a major source of sim-to-real error, particularly on low-cost platforms, where the linear current-to-joint-torque approximation $τ= K_t I$ becomes unreliable because of friction, hysteresis, backlash, and thermal effects. Accurate actuator models can also support force perception and integrated force/position control. We present NeuralActuator, which jointly predicts (i) a torque surrogate for trajectory propagation on low-cost servo platforms, (ii) external forces with a contact-probability gate for sensorless force perception, and (iii) a motor-condition score for a supervised joint, distinguishing normal from mechanically restricted operation. A twin-arm teleoperation system records robot states and actuator telemetry alongside external-force labels, yielding the Neural Actuation Dataset (NAD). The torque-surrogate head is trained through differentiable simulation from pose trajectories without ground-truth joint-torque measurements. A Transformer captures temporal dependencies while enabling real-time inference. We validate NeuralActuator on a 5-DoF OpenManipulator-X, a 6-DoF SO-101 from LeRobot, and a 7-DoF Franka Emika Panda, spanning three actuator families and costs from approximately \$500 to more than \$30{,}000. The low-cost platforms support physically plausible dynamics and force evaluation, while the offline Franka experiment provides a payload-force-estimation benchmark. We also demonstrate motor-condition estimation and improved behavior-cloning performance using NeuralActuator as a pretrained module. We release the dataset, code, and hardware configurations on the project page: https://frank-zy-dou.github.io/projects/NeuralActuator/index.html.
CommentsRSS 2026. Outstanding Systems Paper Award. Project Page: https://people.csail.mit.edu/frankzydou/projects/NeuralActuator/index.html Code: https://github.com/Frank-ZY-Dou/Dynamics-Modeling/tree/main/NeuralActuator
