NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception
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 (i...
| Authors | Zhiyang Dou, John U. Onyemelukwe, Hangxing Zhang et al. |
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| Primary category | cs.RO |
| Published | 13 July 2026 |
| Last updated at source | 17 July 2026 |
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