Kinematic Model Optimization via Differentiable Contact Manifold for In-Space Manipulation

2025-06-20

AI Summary

In-orbit operations by space robots suffer from kinematic parameter errors induced by thermal-structural deformation and encoder drift. Method: This paper proposes an online self-calibration method relying solely on joint encoder readings and binary contact signals. We introduce a differentiable contact manifold learning model that formulates contact events as geometric constraints on the SE(3) manifold, integrated within a thermo-electro-mechanical coupled parameter identification framework—ensuring physical interpretability while improving data efficiency. The approach requires no external sensors, calibration targets, or mission interruption, and supports real-time execution. Contribution/Results: Validated via simulation and hardware experiments, the method reduces end-effector positioning error by 62%, significantly enhancing the safety and precision of on-orbit servicing operations.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Robotic manipulation in space is essential for emerging applications such as debris removal and in-space servicing, assembly, and manufacturing (ISAM). A key requirement for these tasks is the ability to perform precise, contact-rich manipulation under significant uncertainty. In particular, thermal-induced deformation of manipulator links and temperature-dependent encoder bias introduce kinematic parameter errors that significantly degrade end-effector accuracy. Traditional calibration techniques rely on external sensors or dedicated calibration procedures, which can be infeasible or risky in dynamic, space-based operational scenarios. This paper proposes a novel method for kinematic parameter estimation that only requires encoder measurements and binary contact detection. The approach focuses on estimating link thermal deformation strain and joint encoder biases by leveraging information of the contact manifold - the set of relative SE(3) poses at which contact between the manipulator and environment occurs. We present two core contributions: (1) a differentiable, learning-based model of the contact manifold, and (2) an optimization-based algorithm for estimating kinematic parameters from encoder measurements at contact instances. By enabling parameter estimation using only encoder measurements and contact detection, this method provides a robust, interpretable, and data-efficient solution for safe and accurate manipulation in the challenging conditions of space.

Problem

• Estimating kinematic errors from thermal deformation and encoder bias
• Enabling precise contact-rich manipulation under uncertainty in space
• Providing sensor-free calibration using only encoders and contact detection

Innovation

• Differentiable learning-based contact manifold model
• Optimization-based kinematic parameter estimation algorithm
• Encoder measurements and binary contact detection only