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This article focuses on advanced machine learning techniques, specifically Gaussian-parameterized factor graphs, applied to the force estimation problem in continuum robotics. While the application is robotic, the core contribution lies in developing a probabilistic framework that fuses sensor data to solve ill-conditioned inverse problems using dimensionality reduction methods. The work advances the intersection of machine learning and physical modeling for safer robot operation in unstructured environments.
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Multi-Contact Force Estimation for Continuum Robots via Gaussian-Parameterized Factor Graphs

arXiv2 min read

arXiv:2606.29165v2 Announce Type: replace Abstract: Continuum robots offer key advantages in navigating unstructured environments, but their safe operation requires accurate estimation of the external contact forces acting anywhere along the robot body. Estimating these forces at unknown locations is an ill-conditioned problem, particularly for multiple contacts. We propose a unified shape and force estimation framework formulated on a factor graph. By incorporating a Gaussian mixture force parameterization into a discretized probabilistic Cosserat rod model, we reduce the dimensionality of the unknown external forces and mitigate the ill-conditioning of node-wise force estimation. The framework fuses strain, tendon tension, and pose measurements to simultaneously estimate the robot's shape and external forces while accounting for modeling and sensor uncertainties. Numerical simulations demonstrate that the proposed method outperforms existing methods in terms of force location and magnitude estimation for both single and multi-contact scenarios. We further present a progressive variant that introduces basis functions on demand to estimate contact forces sequentially during a simulated confined-navigation task.

Abstract:Continuum robots offer key advantages in navigating unstructured environments, but their safe operation requires accurate estimation of the external contact forces acting anywhere along the robot body. Estimating these forces at unknown locations is an ill-conditioned problem, particularly for multiple contacts. We propose a unified shape and force estimation framework formulated on a factor graph. By incorporating a Gaussian mixture force parameterization into a discretized probabilistic Cosserat rod model, we reduce the dimensionality of the unknown external forces and mitigate the ill-conditioning of node-wise force estimation. The framework fuses strain, tendon tension, and pose measurements to simultaneously estimate the robot's shape and external forces while accounting for modeling and sensor uncertainties. Numerical simulations demonstrate that the proposed method outperforms existing methods in terms of force location and magnitude estimation for both single and multi-contact scenarios. We further present a progressive variant that introduces basis functions on demand to estimate contact forces sequentially during a simulated confined-navigation task.

Robotics (cs.RO)

arXiv:2606.29165 [cs.RO]

(or arXiv:2606.29165v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.29165 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 248 words

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