SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors

TL;DR

This paper introduces SafeSteps, a footstep planning policy for quadrupedal robots that incorporates safety priors through terrain analysis, resulting in safer and more efficient locomotion in rough terrains.

Contribution

It presents a novel model-based approach that encodes terrain safety information into footstep planning, reducing safety violations and improving policy performance.

Findings

Significantly fewer safety violations during training and deployment

Enhanced sample efficiency and final performance of the policy

Effective in diverse rough terrain scenarios

Abstract

We present a footstep planning policy for quadrupedal locomotion that is able to directly take into consideration a-priori safety information in its decisions. At its core, a learning process analyzes terrain patches, classifying each landing location by its kinematic feasibility, shin collision, and terrain roughness. This information is then encoded into a small vector representation and passed as an additional state to the footstep planning policy, which furthermore proposes only safe footstep location by applying a masked variant of the Proximal Policy Optimization algorithm. The performance of the proposed approach is shown by comparative simulations and experiments on an electric quadruped robot walking in different rough terrain scenarios. We show that violations of the above safety conditions are greatly reduced both during training and the successive deployment of the policy, resulting in an inherently safer footstep planner. Furthermore, we show how, as a byproduct, fewer reward terms are needed to shape the behavior of the policy, which in return is able to achieve both better final performances and sample efficiency.