NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception

Abstract

Differentiable simulators have advanced policy learning and model-based control across diverse robotic tasks. To date, actuator dynamics remain underexplored and are a major source of sim-to-real error, especially on low-cost platforms where the linear current–torque model τ = K_tI breaks down under commanded-target tracking due to friction, hysteresis, backlash, and thermal effects. Beyond forward dynamics, accurate actuator models also support force perception, which is crucial for jointly modeling force and position control in manipulation tasks. We present NeuralActuator, a neural actuator model that jointly predicts (i) torques to capture the full nonlinear and time-varying current–torque relationship on low-cost servos, (ii) external contact forces as well as force detection gates for sensorless force perception, and (iii) motor conditions indicating their operating regime. We introduce a twin-arm teleoperation system that collects motor states alongside ground-truth forces from interactions and known external forces, contributing a dataset named Neural Actuation Dataset (NAD). NeuralActuator is trained through differentiable simulation using only pose trajectories as supervision, eliminating the need for torque sensors. A Transformer-based architecture captures temporal dependencies while enabling efficient real-time inference. We validate NeuralActuator across three platforms—a 5-DoF OpenManipulator-X, a 6-DoF SO-101 from LeRobot, and a 7-DoF industrial arm with on-board joint torque sensors—spanning three actuator families and the $500–$30k+ cost spectrum, and show that it enables accurate dynamics modeling, sensorless force estimation, motor condition estimation, and improved behavior-cloning control when used as a pretrained module. Our system and datasets will be released.