Heuristic Step Planning for Learning Dynamic Bipedal Locomotion: A Comparative Study of Model-Based and Model-Free Approaches

TL;DR

This paper introduces a heuristic step-planning framework for bipedal robots that achieves high accuracy, robustness, and energy efficiency without relying on complex analytical models, outperforming traditional model-based methods.

Contribution

The study presents a heuristic, model-free approach to bipedal locomotion that simplifies planning while maintaining or improving performance compared to model-based controllers.

Findings

Achieves up to 80% accuracy in velocity tracking.

Over 50% improvement in robustness on uneven terrain.

Enhances energy efficiency in bipedal walking.

Abstract

This work presents an extended framework for learning-based bipedal locomotion that incorporates a heuristic step-planning strategy guided by desired torso velocity tracking. The framework enables precise interaction between a humanoid robot and its environment, supporting tasks such as crossing gaps and accurately approaching target objects. Unlike approaches based on full or simplified dynamics, the proposed method avoids complex step planners and analytical models. Step planning is primarily driven by heuristic commands, while a Raibert-type controller modulates the foot placement length based on the error between desired and actual torso velocity. We compare our method with a model-based step-planning approach -- the Linear Inverted Pendulum Model (LIPM) controller. Experimental results demonstrate that our approach attains comparable or superior accuracy in maintaining target velocity (up to 80%), significantly greater robustness on uneven terrain (over 50% improvement), and improved energy efficiency. These results suggest that incorporating complex analytical, model-based components into the training architecture may be unnecessary for achieving stable and robust bipedal walking, even in unstructured environments.