Identification of a Physics-Based Electrical Power Consumption Model for the Unitree G1 Humanoid Arm

2026-06-14

AI Summary

This work proposes a physics-based, linearly parameterized electrical power consumption model to enable energy-aware motion planning and thermal management for humanoid robot arms. The model explicitly accounts for actuator losses, gravity-compensated payload effects, and inter-joint coupling dynamics, with its parameters identified via linear regression using onboard power measurements. Trained on 897 trajectories, the model achieves an R² of 0.933 and an RMSE of 1.07 W, and demonstrates strong generalization with an R² of 0.965 on 46 unseen validation trajectories featuring novel velocity profiles. The results not only confirm high predictive accuracy but also reveal distinct dominant loss mechanisms across individual joints.

Abstract

Accurate prediction of electrical power consumption is essential for energy-aware motion planning, battery management, and thermal monitoring in battery-powered humanoid robots. This letter presents a physics-based, linear-in-parameters model for the electrical power consumption of the seven-degree-of-freedom left arm of the Unitree~G1 humanoid robot. The proposed formulation combines actuator loss terms with a baseline-torque correction that captures changes in gravity-compensation load and enables accurate prediction of negative net power trajectories. Pairwise interaction terms are introduced to model power coupling during simultaneous multi-joint motion. Model parameters are identified from experimental data collected on a physical Unitree~G1 using onboard power measurements as the regression target. Across 897 trajectories covering single-joint and coordinated arm motions at multiple speed levels, the identified model achieves $R^2 = 0.933$ with an RMSE of 1.07 (W). Validation on 46 trajectories executed at previously unseen speeds yields $R^2 = 0.965$, demonstrating strong generalisation beyond the identification dataset. Analysis of the identified parameters reveals distinct power-consumption characteristics across the arm, with viscous friction dominating most joints (shoulder pitch and all three wrist joints), copper losses dominating shoulder yaw and the elbow, and shoulder roll uniquely dominated by Coulomb friction.

Problem

• power consumption
• humanoid robot
• energy-aware motion planning
• battery management
• thermal monitoring

Innovation

• physics-based power modeling
• parameter identification
• multi-joint power coupling
• energy-aware robotics
• humanoid arm dynamics