Trajectory-based actuator identification via differentiable simulation
For robotics practitioners needing accurate actuator models without dedicated test stands or torque sensors, this method offers a practical, sensor-free identification approach that significantly improves simulation-to-real transfer.
This work presents a trajectory-based actuator identification method using differentiable simulation to fit actuator models from encoder motion alone, without torque sensors. On real-robot trajectories, it reduces mean absolute position error from 14.20 mrad to 7.54 mrad (1.88× improvement) and improves downstream locomotion performance by 46% in travel distance and 75% in rotational deviation.
Accurate actuation models are critical for bridging the gap between simulation and real robot behavior, yet obtaining high-fidelity actuator dynamics typically requires dedicated test stands and torque sensing. We present a trajectory-based actuator identification method that uses differentiable simulation to fit system-level actuator models from encoder motion alone. Identification is posed as a trajectory-matching problem: given commanded joint positions and measured joint angles and velocities, we optimize actuator and simulator parameters by backpropagating through the simulator, without torque sensors, current/voltage measurements, or access to embedded motor-control internals. The framework supports multiple model classes, ranging from compact structured parameterizations to neural actuator mappings, within a unified optimization pipeline. On held-out real-robot trajectories for a high-gear-ratio actuator with an embedded PD controller, the proposed torque-sensor-free identification achieves much tighter trajectory alignment than a supervised stand-trained baseline dominated by steady-state data, reducing mean absolute position error from 14.20 mrad to as low as 7.54 mrad (1.88 times). Finally, we demonstrate downstream impact for the same actuator class in a real-robot locomotion study: training policies with the refined actuator model increases travel distance by 46% and reduces rotational deviation by 75% relative to the baseline.