Actuator Digital Twins for Predictive Robotic Simulation: Experimental Validation and Multi-DOF Scalability

Abstract

Accurate actuator modeling is critical for robust design validation and sim-to-real control transfer in humanoid robotics. Yet, in practice, developers rely on simplified actuator models built from sparse datasheets or offline system identification, which often omit internal control logic, saturation, sensor dynamics, and electromechanical actuator dynamics. This limits model fidelity under changing conditions and contributes to sim-to-real failures. We propose actuator Digital Twins (DTs) as a scalable solution for predictive simulation. In this work, predictive simulation is defined as the forward computation of joint position and actuator torque from prescribed reference trajectories, controller parameters, mechanical configuration, and initial conditions, with prediction accuracy evaluated against measurements from the physical actuator. We validate a DT of the Pulsar PULSE115 quasi-direct-drive actuator that reproduces the actuator electromechanical dynamics, physical operating limits, sensing characteristics, and embedded cascaded controller executed at 10 kHz on a 1-DOF pendulum testbed, comparing real-world experiments with simulations using both the DT and a simplified model. Across varying trajectories and configurations, the DT maintains low error-from-real, while the simplified model degrades outside its tuned regime, particularly under changes in trajectory dynamics, mechanical load, and controller gains. We further embed the DT in a 4-DOF humanoid arm simulation and show that it runs significantly faster than the real-time version, achieving a simulation speedup factor of approximately 6.3× on a standard laptop. These results demonstrate that actuator-specific electromechanical and embedded control modeling improves the forward prediction of physical actuator behavior while remaining computationally practical for multi-joint robotic simulation.