Data-Driven Autoregressive Power Prediction for GTernal Robots in the Robotarium

Yassin Abdelmeguid; Ammar Hasan

cs.ROMar 14, 2026

Data-Driven Autoregressive Power Prediction for GTernal Robots in the Robotarium

Authors: Yassin Abdelmeguid, Ammar Hasan

Organizations: American University of Sharjah

Abstract

Energy-aware algorithms for multi-robot systems require accurate power consumption models, yet existing approaches rely on kinematic approximations that fail to capture the complex dynamics of real hardware. We present a lightweight autoregressive predictor for the GTernal mobile robot platform deployed in the Georgia Tech Robotarium. Through analysis of 48,000 samples collected across six motion trials, we discover that power consumption exhibits strong temporal autocorrelation ( $ρ_1 = 0.95$ ) that dominates kinematic effects. A 7,041-parameter multi-layer perceptron (MLP) achieves $R^2 = 0.90$ on held-out motion patterns by conditioning on recent power history, reaching the theoretical prediction ceiling imposed by measurement noise. Physical validation across seven robots in a collision avoidance scenario yields mean $R^2 = 0.87$ , demonstrating zero-shot transfer to unseen robots and behaviors. The predictor runs in 224 $μ$ s per inference, enabling real-time deployment at 150 $\times$ the platform's 30 Hz control rate. We release the trained model and dataset to support energy-aware multi-robot algorithm development.

Figures & tables

Figure 1: Power consumption autocorrelation across six motion trials. All trials exhibit lag-1 correlation $\rho_{1}>0.94$ , with the structure persisting across diverse motion patterns. This strong temporal dependence motivates autoregressive prediction.

Figure 2: Time series of predicted and actual power over a 20-second segment from the structured motion trial. The predictor tracks rapid transitions and sustained levels with minimal lag.

Figure 3: Predicted versus actual power consumption on the held-out steady-state trial. Points cluster tightly around the identity line with $R^{2}=0.92$ and MAE = 31.5 mW.

Figure 4: Distribution of prediction residuals across all trials. The distribution is approximately Gaussian with mean 1.3 mW and 90% of errors falling within $[-47,50]$ mW.

Figure 5: Per-robot $R^{2}$ (left) and MAE (right) during multi-robot collision avoidance. The predictor achieves mean $R^{2}=0.87$ across seven robots despite training on a single unit.

Autoregressive Modeling

Autoregressive modeling predicts sequences step by step from prior context, spanning language, time series, images, audio, and scientific simulations.

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Jun 14, 2026J/K move · Enter open · S save

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

Engineering Department at Harper Adams University, Newport, Shropshire TF10 8NB, UK · Lincoln Institute for Agri-Food Technology and Lincoln Centre for Autonomous Systems

Robotics System Identification Humanoid Robotics

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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.

Abstract

Autoregressive Modeling

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