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

Nestor N. Deniz; Sebastian Vega; Simon Parsons; Fernando Auat Cheein

cs.ROJun 14, 2026

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

Authors: Nestor N. Deniz, Sebastian Vega, Simon Parsons, Fernando Auat Cheein

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 UnitreeG1 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 UnitreeG1 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.

Explore similar work

Jun 14, 2026cs.RO

Energy-Efficient Arm Reaching for a Humanoid Robot via Deep Reinforcement Learning with Identified Power Models

Humanoid robots performing in-field manipulation tasks, such as robotic apple harvesting, face severe energy constraints that directly limit the number of reaching motions that can be executed per battery charge. 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, combining a physics-based, experimentally identified electrical power model with a Soft Actor-Critic (SAC) policy trained in a Pinocchio-based rigid-body dynamics simulator. The RL policy operates on an incremental joint-position action space and is trained with a Hybrid Constellation Reward that combines a four-point end-effector constellation distance with a torque-norm energy proxy; after % $5\times10^6$ training it reaches a $69.9\%$ success rate over $1\,000$ random targets in kinematic simulation, at a mean energy of \SI{98.16}{\joule} on successful episodes. Finally, on the physical UnitreeG1, 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 1.33^\circ

-- within the \SI{4}{\centi\metre}/

8.6^\circ

training tolerance. These results constitute a first step toward energy-aware reinforcement-learning-based arm reaching for humanoid robots.

Nestor N. Deniz, Simon Parsons, Fernando Auat Cheein

Humanoid Robots Actuator Agricultural Robots Arms

Ask PDF

Jun 2, 2026cs.RO

Hybrid Dynamics Modeling for a Flexible 2-DoF Robotic Arm

This paper examines three approaches for modeling the dynamics of a flexible-link 2-DoF robotic arm to address unmodeled dynamics not captured by rigid-body models. Two physics informed models combine rigid-body dynamics (RBD) formulations with a Gaussian Mixture Model (GMM) to capture residual model errors and linkage flexibility. A kinematics-based regression model serves as a purely data-driven baseline. Using an open-source dataset, torque predictions are first estimated using Ridge regression on kinematic features, while the physicsbased baseline is constructed from published specifications, and ordinary least-squares regression is subsequently used to estimate the same parameter set directly from data. Results show that the physics-based parameters yield the poorest accuracy, while regularized and least-squares estimators align more closely with measured torques. Residual analysis and error metrics highlight the limitations of purely parametric models for flexible-link systems and underscore the value of regularization and data-driven identification, supporting developments of semi-parametric residual learning methods.

Maciek Popik, Daniel Yang, Mahdis Bisheban

Rigid-Body Dynamics Actuator Kinematics Robotic Manipulator System Identification

Ask PDF

Jul 21, 2026cs.LG

Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations. Our results show that Bayesian linear regression based on this physics-aware model can improve the reliability of the expected energy consumption, as compared with standard linear regression. Further, it is shown that more complex machine learning models such as neural networks and gradient boosted regression trees, based on the same physical model, can further improve the accuracy in energy forecasting and significantly outperform standard versions of the same machine learning models. In addition to point predictions of the energy consumption, we develop a framework for estimating the corresponding uncertainty in the form of predicted standard deviation. Our results show that all of the models learn to estimate the uncertainty reasonably well.

Hannes Nilsson, Rafael Basso, Balázs Kulcsár +1

Energy Forecasting Electric Vehicles Predictive Uncertainty Physics-Aware Models Machine Learning

Ask PDF