用深度强化学习从预生成步态库中选取并优化步态，实现稳定高效行走控制。
NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
- 从离线生成的步态库中选择并微调步态，结合轨迹优化与强化学习优势
- 训练速度比传统方法快，生成动作最接近原始参考步态
- 适合需要快速适应复杂环境的双足机器人研发者
强化学习（RL）已成为学习双足行走鲁棒控制策略的强大工具，但其奖励设计复杂且不直观，难以调控期望行为。相比之下，基于轨迹优化的方法在高维腿部系统中提供了更可调节、可解释且数学基础坚实的运动规划。然而，这些方法对真实世界扰动（如外部冲击）往往脆弱。本文提出NaviGait，一种分层框架，将轨迹优化的结构与强化学习的适应性相结合，实现鲁棒且直观的行走控制。NaviGait利用强化学习从离线生成的步态库中选择、最小化变形并稳定步态，合成新运动。实验表明，该方法生成的行走策略能良好匹配参考运动，同时保持与其他控制器相当的鲁棒性；更重要的是，其结构大幅简化了强化学习奖励设计。相比传统及基于模仿的强化学习，NaviGait显著加快训练速度，并使生成动作最接近原始参考。通过解耦高层运动生成与底层修正，NaviGait为实现动态、鲁棒的行走提供了更具扩展性和通用性的方案。视频与完整框架已公开于 https://dynamicmobility.github.io/navigait/
原文摘要 · Abstract (English)
Reinforcement learning (RL) has emerged as a powerful method to learn robust control policies for bipedal locomotion. Yet, it can be difficult to tune desired robot behaviors due to unintuitive and complex reward design. In comparison, trajectory optimization-based methods offer more tuneable, interpretable, and mathematically grounded motion plans for high-dimensional legged systems. However, these methods often remain brittle to real-world disturbances like external perturbations. In this work, we present NaviGait, a hierarchical framework that combines the structure of trajectory optimization with the adaptability of RL for robust and intuitive locomotion control. NaviGait leverages RL to synthesize new motions by selecting, minimally morphing, and stabilizing gaits taken from an offline-generated gait library. NaviGait results in walking policies that match the reference motion well while maintaining robustness comparable to other locomotion controllers. Additionally, the structure imposed by NaviGait drastically simplifies the RL reward composition. Our experimental results demonstrate that NaviGait enables faster training compared to conventional and imitation-based RL, and produces motions that remain closest to the original reference. Overall, by decoupling high-level motion generation from low-level correction, NaviGait offers a more scalable and generalizable approach for achieving dynamic and robust locomotion. Videos and the full framework are publicly available at https://dynamicmobility.github.io/navigait/
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