用启发式步态规划实现人形机器人稳定行走，无需复杂模型。
Heuristic Step Planning for Learning Dynamic Bipedal Locomotion: A Comparative Study of Model-Based and Model-Free Approaches
- 通过速度误差驱动的启发式步态规划，简化控制逻辑。
- 在不平地形上鲁棒性提升超50%，能耗更低，速度跟踪精度达80%。
- 适合需要低成本、高适应性的机器人动态行走场景。
本文提出一种基于学习的双足行走扩展框架，采用由目标躯干速度追踪引导的启发式步态规划策略。该框架使类人机器人能精确与环境交互，支持跨隙和精准接近目标等任务。与依赖完整或简化动力学的方法不同，本方法避免使用复杂步态规划器和解析模型，主要依靠启发式指令进行步态规划，同时采用Raibert型控制器根据实际与目标躯干速度误差调节脚位长度。实验对比了基于模型的步态规划方法（线性倒立摆模型，LIPM控制器）。结果表明，本方法在维持目标速度方面达到或超过80%精度，不平地形鲁棒性提升超过50%，且能量效率更高。这些结果表明，在非结构化环境中实现稳定鲁棒双足行走，未必需要将复杂的分析性建模组件引入训练架构。
原文摘要 · Abstract (English)
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.
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