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

2025-11-02

AI Summary

To address the challenge of enabling bipedal robots to achieve stable, robust dynamic locomotion in unstructured environments while supporting precise environmental interactions (e.g., obstacle negotiation, target approach), this paper proposes a model-free heuristic gait learning framework. The method employs desired torso velocity as a high-level reference signal and dynamically modulates footstep placement via a Raibert-style controller—without requiring analytical dynamics models or complex gait planners. Reinforcement learning is further integrated to adapt step length in real time based on torso velocity tracking error. Experimental results demonstrate that, compared to conventional linear inverted pendulum model (LIPM)-based approaches, the proposed framework improves target velocity tracking accuracy by 80%, enhances robustness on uneven terrain by over 50%, and significantly improves energy efficiency. Overall, it matches or surpasses model-based baselines in comprehensive performance.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Developing heuristic step planning for dynamic bipedal locomotion learning
• Comparing model-based and model-free approaches for bipedal walking
• Enhancing robot-environment interaction for gap crossing and target approach

Innovation

• Heuristic step planning strategy for bipedal locomotion
• Raibert-type controller modulates foot placement length
• Model-free approach avoids complex analytical components