STATE-NAV: Stability-Aware Traversability Estimation for Bipedal Navigation on Rough Terrain

TL;DR

This paper introduces a learning-based framework for bipedal robot navigation on rough terrain, emphasizing stability-aware traversability estimation and risk-sensitive planning to improve robustness and efficiency.

Contribution

It presents the first learning-based stability-aware traversability estimation for bipedal robots, integrating a transformer network with hierarchical planning for improved navigation.

Findings

Enhanced robustness in rough terrain navigation

Improved time efficiency over existing methods

Validated in simulation and real-world environments

Abstract

Bipedal robots have advantages in maneuvering human-centered environments, but face greater failure risk compared to other stable mobile platforms such as wheeled or quadrupedal robots. While learning-based traversability has been widely studied for these platforms, bipedal traversability has instead relied on manually designed rules with limited consideration of locomotion stability on rough terrain. In this work, we present the first learning-based traversability estimation and risk-sensitive navigation framework for bipedal robots operating in diverse, uneven environments. TravFormer, a transformer-based neural network, is trained to predict bipedal instability with uncertainty, enabling risk-aware and adaptive planning. Based on the network, we define traversability as stability-aware command velocity-the fastest command velocity that keeps instability below a user-defined limit. This velocity-based traversability is integrated into a hierarchical planner that combines traversability-informed Rapid Random Tree Star (TravRRT*) for time-efficient planning and Model Predictive Control (MPC) for safe execution. We validate our method in MuJoCo simulation and the real world, demonstrating improved navigation performance, with enhanced robustness and time efficiency across varying terrains compared to existing methods.