Towards Terrain-Aware Safe Locomotion for Quadrupedal Robots Using Proprioceptive Sensing

2026-03-10

AI Summary

This work addresses the challenge of reliable terrain estimation and safe control for quadrupedal robots operating in unstructured environments using only proprioceptive sensing—namely IMUs, joint encoders, and contact force sensors. The authors propose a unified framework that jointly estimates terrain, robot state, and contact events by constructing a 2.5D elevation map and extracting supporting surface parameters. This approach fuses historical terrain data with real-time proprioceptive measurements to inform a hierarchical safety controller based on Control Barrier Functions (CBFs). Notably, it is the first method to integrate these three estimation tasks directly into CBF-based safety-critical control under low-cost sensing constraints. Experimental results demonstrate a 64.8% reduction in mean absolute error and a 47.2% decrease in variance for base pose estimation, more robust contact detection, and effective avoidance of hazardous regions and body-terrain collisions.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Achieving safe quadrupedal locomotion in real-world environments has attracted much attention in recent years. When walking over uneven terrain, achieving reliable estimation and realising safety-critical control based on the obtained information is still an open question. To address this challenge, especially for low-cost robots equipped solely with proprioceptive sensors (e.g., IMUs, joint encoders, and contact force sensors), this work first presents an estimation framework that generates a 2.5-D terrain map and extracts support plane parameters, which are then integrated into contact and state estimation. Then, we integrate this estimation framework into a safety-critical control pipeline by formulating control barrier functions that provide rigorous safety guarantees. Experiments demonstrate that the proposed terrain estimation method provides smooth terrain representations. Moreover, the coupled estimation framework of terrain, state, and contact reduces the mean absolute error of base position estimation by 64.8%, decreases the estimation variance by 47.2%, and improves the robustness of contact estimation compared to a decoupled framework. The terrain-informed CBFs integrate historical terrain information and current proprioceptive measurements to ensure global safety by keeping the robot out of hazardous areas and local safety by preventing body-terrain collision, relying solely on proprioceptive sensing.

Problem

• quadrupedal locomotion
• terrain estimation
• proprioceptive sensing
• safety-critical control
• uneven terrain

Innovation

• proprioceptive sensing
• terrain estimation
• control barrier functions
• quadrupedal locomotion
• safety-critical control