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.

翻译：本研究提出了一种扩展的基于学习的双足运动框架，该框架融合了一种由期望躯干速度跟踪引导的启发式步态规划策略。该框架实现了人形机器人与其环境之间的精确交互，支持跨越间隙和准确接近目标物体等任务。与基于完整或简化动力学的方法不同，所提方法避免了复杂的步态规划器和解析模型。步态规划主要由启发式指令驱动，同时一个Raibert型控制器根据期望与实际躯干速度之间的误差调节足部落点长度。我们将本方法与一种基于模型的步态规划方法——线性倒立摆模型（LIPM）控制器进行了比较。实验结果表明，我们的方法在维持目标速度方面达到了相当或更优的精度（最高达80%），在不平坦地形上表现出显著更高的鲁棒性（提升超过50%），并改善了能量效率。这些结果表明，即使在非结构化环境中，为实现稳定且鲁棒的双足行走，在训练架构中引入复杂的解析性、基于模型的组件可能并非必需。