Proprioceptive-only State Estimation for Legged Robots with Set-Coverage Measurements of Learned Dynamics

2026-03-18

AI Summary

This work addresses the challenge of inconsistent and divergent state estimation in legged robots when relying solely on proprioception under non-Gaussian noise. To this end, we propose a robust estimation framework that integrates a learned dynamics model with set-based uncertainty modeling. The approach leverages historical joint-level measurements to learn system dynamics and characterizes measurement uncertainty through a distribution-free set-membership representation, which is then fused with a Gaussian filter to yield stable state estimates. Experimental evaluations on both simulated and real-world quadrupedal robot datasets demonstrate that the proposed method significantly reduces state drift and maintains consistent, robust performance compared to conventional Gaussian baseline estimators.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Proprioceptive-only state estimation is attractive for legged robots since it is computationally cheaper and is unaffected by perceptually degraded conditions. The history of joint-level measurements contains rich information that can be used to infer the dynamics of the system and subsequently produce navigational measurements. Recent approaches produce these estimates with learned measurement models and fuse with IMU data, under a Gaussian noise assumption. However, this assumption can easily break down with limited training data and render the estimates inconsistent and potentially divergent. In this work, we propose a proprioceptive-only state estimation framework for legged robots that characterizes the measurement noise using set-coverage statements that do not assume any distribution. We develop a practical and computationally inexpensive method to use these set-coverage measurements with a Gaussian filter in a systematic way. We validate the approach in both simulation and two real-world quadrupedal datasets. Comparison with the Gaussian baselines shows that our proposed method remains consistent and is not prone to drift under real noise scenarios.

Problem

• proprioceptive-only state estimation
• legged robots
• measurement noise
• Gaussian assumption
• state consistency

Innovation

• proprioceptive-only state estimation
• set-coverage measurements
• learned dynamics
• non-Gaussian noise
• legged robots