Humanoid Robot Whole-body Geometric Calibration with Embedded Sensors and a Single Plane

2025-07-22

AI Summary

To address the time-consuming calibration process and high experimental burden associated with whole-body geometric calibration of humanoid robots, this paper proposes a fully automated, minimally intrusive calibration method. The approach leverages single-plane contact constraints and integrates embedded six-axis force-torque sensors with an admittance controller to achieve closed-loop, hands-free calibration. We introduce the Information-Rich Optimal Configuration (IROC) algorithm, which automatically selects a minimal optimal set of calibration poses from a candidate pool via normalized weighted information matrix analysis and information entropy evaluation. Experimental validation on the TALOS humanoid robot demonstrates that full kinematic parameter calibration is achieved using only 31 three-point contact configurations. Cross-validation yields a root-mean-square error 2.3× lower than that of the manufacturer’s nominal model, significantly improving motion accuracy and simulation fidelity.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Whole-body geometric calibration of humanoid robots using classical robot calibration methods is a timeconsuming and experimentally burdensome task. However, despite its significance for accurate control and simulation, it is often overlooked in the humanoid robotics community. To address this issue, we propose a novel practical method that utilizes a single plane, embedded force sensors, and an admittance controller to calibrate the whole-body kinematics of humanoids without requiring manual intervention. Given the complexity of humanoid robots, it is crucial to generate and determine a minimal set of optimal calibration postures. To do so, we propose a new algorithm called IROC (Information Ranking algorithm for selecting Optimal Calibration postures). IROC requires a pool of feasible candidate postures to build a normalized weighted information matrix for each posture. Then, contrary to other algorithms from the literature, IROC will determine the minimal number of optimal postures that are to be played onto a robot for its calibration. Both IROC and the single-plane calibration method were experimentally validated on a TALOS humanoid robot. The total whole-body kinematics chain was calibrated using solely 31 optimal postures with 3-point contacts on a table by the robot gripper. In a cross-validation experiment, the average root-mean-square (RMS) error was reduced by a factor of 2.3 compared to the manufacturer's model.

Problem

• Calibrate humanoid robot kinematics without manual intervention
• Select minimal optimal postures for efficient calibration
• Improve accuracy using single plane and embedded sensors

Innovation

• Uses single plane and embedded force sensors
• Employs IROC algorithm for optimal postures
• Reduces calibration error by 2.3 times