Forecasting Terrain Traversal in Legged Exploration Robots with Adaptive Locomotion Policies

Abstract

The deployment of legged robots in unstructured and hazardous environments necessitates advanced predictive capabilities to ensure safe and efficient terrain traversal. While adaptive locomotion policies have significantly improved the robustness of legged systems in navigating complex topographies, accurately predicting the success or failure of these policies prior to physical execution remains a critical challenge. This paper presents a comprehensive comparative analysis of terrain traversal prediction methodologies derived from adaptive locomotion policies. By systematically evaluating proprioceptive and exteroceptive data streams integrated within reinforcement learning frameworks, this study identifies the latent features most indicative of traversal success across varied terrains such as gravel, mud, steep slopes, and discrete stairs. The research methodology employs a modular predictive architecture that maps the internal state representations of trained locomotion policies to external traversal outcomes. Extensive simulations demonstrate that incorporating policy specific behavioral metrics significantly enhances prediction accuracy compared to purely geometric terrain assessments. The findings suggest that the internal dynamics of adaptive policies contain implicit representations of terrain traversability, which can be extracted and utilized for high level path planning and risk mitigation. This research contributes to the broader field of autonomous exploration by bridging the gap between low level motor control and high level cognitive planning, ultimately enabling more autonomous, resilient, and intelligent legged robotic systems capable of sustained operation in unknown environments.