NeuralActuator 让几百块的机器人也能感知外力，不用额外传感器。它缩小了仿真和现实的差距，还能提升模仿学习效果。
NeuralActuator 联合预测力矩替代、外力和电机状态，解决低成本机器人平台电流-力矩关系不可靠问题。研究引入 NAD 数据集，通过双臂遥测系统记录状态和外力标签。该模型在 OpenManipulator-X（5-DoF）、SO-101（6-DoF）和 Franka Emika Panda（7-DoF）上评估，平台成本从 500 美元到 3 万美元以上。低成本平台支持动力学和力评估，Franka 实验提供额外基准。作为预训练模块，NeuralActuator 可提升行为克隆性能。
NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception
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.