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

• URL: http://arxiv.org/abs/2606.15915v1
• Date: Sun, 14 Jun 2026 16:45:07 GMT
• Title: Identification of a Physics-Based Electrical Power Consumption Model for the Unitree G1 Humanoid Arm
• Abstract summary: This letter presents a physics-based, linear-in- parameters model for the electrical power consumption of the left arm of the UnitreeG1 humanoid robot.<n>The proposed formulation combines actuator loss terms with a baseline-torque correction that captures changes in gravity-compensation load.<n>Across 897 trajectories covering single-joint and coordinated arm motions at multiple speed levels, the identified model achieves $R2 = 0.933$ with an RMSE of 1.07 (W)
• Score: 41.90412270641482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Hip Energized Monopedal Hopping [0.5505634045241289]
  We present a novel stepping strategy for pitch unlocked planar monopeds.<n>Our controller increases the pitch stabilization torque, thereby adding energy to the gait.<n>A new stepping policy adjusts the distribution of energy between the radial and angular degrees of freedom to counteract dissipative losses.
  arXiv  Detail & Related papers  (2026-08-11T02:28:04Z)
• Friction Characterization of a Cable-Driven Differential Actuation System for Lower-Limb Exoskeletons [42.628421392139]
  Lower-limb exoskeletons require actuation systems that can provide accurate joint torque control.<n> Conventional architectures often rely on independently actuated joints and joint-level torque sensors.<n>This paper presents a novel differential actuation architecture for hip-knee flexion/extension.
  arXiv  Detail & Related papers  (2026-06-14T19:47:32Z)
• Energy-Efficient Arm Reaching for a Humanoid Robot via Deep Reinforcement Learning with Identified Power Models [44.9566685062534]
  This paper presents an end-to-end, energy-aware reinforcement learning framework for the 7-degree-of-freedom left arm of the UnitreeG1 humanoid robot.<n>The policy is validated over three independent 10-target batches, achieving a mean energy of $71.5 pm 48.3$,J, an end-effector position error of $2.64 pm 1.04$,cm, and an orientation error of $6.92 pm 1.33circ$ -- within the SI4centimetre/$8.6circ$ training tolerance.
  arXiv  Detail & Related papers  (2026-06-14T16:46:18Z)
• Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control [46.47619556874003]
  We propose a whole-body controller that combines a model-based admittance control for the manipulator arm with a Reinforcement Learning policy for legged locomotion.<n>The admittance controller maps external wrenches--such as those applied by a human during physical interaction--into desired end-effector velocities, allowing for compliant behavior.<n>We validate our approach in both simulation and hardware using the Unitree Go2 quadruped robot with a 6-DoF arm and wrist-mounted 6-DoF Force/Torque sensor.
  arXiv  Detail & Related papers  (2026-03-02T22:31:12Z)
• System Identification under Constraints and Disturbance: A Bayesian Estimation Approach [5.58001571731715]
  We introduce a Bayesian system identification (SysID) framework for jointly estimating robot's state trajectories and physical parameters with high accuracy.<n>It embeds physically consistent inverse dynamics, contact and loop-closure constraints, and fully featured joint friction models as hard, stage-wise equality constraints.
  arXiv  Detail & Related papers  (2026-02-18T10:45:30Z)
• RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking [50.200035833530876]
  RobotDancing is a simple, scalable framework that predicts residual joint targets to explicitly correct dynamics discrepancies.<n>It can track multi-minute, high-energy behaviors (jumps, spins, cartwheels) and deploys zero-shot to hardware with high motion tracking quality.
  arXiv  Detail & Related papers  (2025-09-25T03:30:34Z)
• A Hybrid Surrogate for Electric Vehicle Parameter Estimation and Power Consumption via Physics-Informed Neural Operators [1.4524691281000133]
  We present a hybrid surrogate model for electric vehicle parameter estimation and power consumption.<n>From speed and acceleration alone, it outputs time-varying motor and regenerative braking efficiencies.<n>We evaluate on real-world logs from a Tesla Model 3, Tesla Model S, and the Kia EV9.
  arXiv  Detail & Related papers  (2025-08-18T04:01:42Z)
• Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction [1.1270209626877075]
  The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations.<n>Traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed.<n>Data-driven machine learning methods present a promising avenue for improving wind turbine modeling.
  arXiv  Detail & Related papers  (2025-02-11T08:16:48Z)
• Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic Computing [41.66366715982197]
  We build a general model of memristors suitable for the simulation of event-based systems. We extend an existing general model of memristors to an event-driven setting. We demonstrate an approach for fitting the parameters of the event-based model to the drift model.
  arXiv  Detail & Related papers  (2024-06-14T13:17:19Z)
• End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control [16.88250694156719]
  We present a novel hybrid model formulation that enables us to identify fully physically consistent inertial parameters of a rigid body dynamics model. We compare our approach against state-of-the-art inverse dynamics models on a 7 degree of freedom manipulator.
  arXiv  Detail & Related papers  (2022-05-27T07:39:28Z)
• Cat states in a driven superfluid: role of signal shape and switching protocol [62.997667081978825]
  We investigate the behavior of a one-dimensional Bose-Hubbard model whose kinetic energy is made to oscillate with zero time-average. We analyze the robustness of this unconventional ground state against variations of a number of system parameters.
  arXiv  Detail & Related papers  (2020-05-11T15:15:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.