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.

翻译：电力消耗的精确预测对于电池供电仿人机器人的能量感知运动规划、电池管理和热监测至关重要。本文针对Unitree G1仿人机器人七自由度左臂的电力消耗，提出了一种基于物理的线性参数模型。所提出的公式将执行器损失项与基座扭矩校正相结合，该校正捕捉了重力补偿负载的变化，并能够精确预测负净功率轨迹。引入两两交互项来模拟多关节同步运动时的功率耦合。模型参数通过在实际Unitree G1机器人上收集的实验数据进行识别，将机载功率测量值作为回归目标。在涵盖单关节与协调臂运动、多种速度级别的897条轨迹中，识别模型达到了$R^2 = 0.933$，均方根误差为1.07瓦特。在46条以未见速度执行的轨迹上进行的验证获得了$R^2 = 0.965$，展示了超越识别数据集的强大泛化能力。对识别参数的分析揭示了臂部不同的电力消耗特征：黏性摩擦主导了多数关节（肩部俯仰及所有三个腕部关节），铜损主导了肩部偏航和肘部，而肩部滚转则唯一由库仑摩擦主导。