HWC-Loco：一种面向鲁棒人形机器人运动的分层全身控制方法
HWC-Loco: A Hierarchical Whole-Body Control Approach to Robust Humanoid Locomotion
AI总结：
针对人形机器人控制模型在环境差异下鲁棒性不足、安全与任务完成度难平衡的问题，提出分层全身控制算法HWC-Loco，通过鲁棒优化与分层策略动态权衡安全与任务，在多场景实验中性能优于现有先进模型。
摘要：
人形机器人能够在各类工作场景中承担人类角色，已成为具身智能的核心组成部分。然而，作为物理结构复杂的机器人，学习一套能在多样环境中稳定运行的控制模型本身就极具挑战，尤其是在训练环境与部署环境存在差异的情况下。本研究提出HWC-Loco，一种专门针对人形机器人运动任务的鲁棒全身控制算法。通过将策略学习重构为鲁棒优化问题，HWC-Loco可显式学习从安全关键场景中恢复的能力。在优先保障安全性的同时，过于保守的行为会削弱机器人完成既定任务的能力。为解决这一难题，HWC-Loco采用分层策略实现鲁棒控制，该策略可在人类行为规范与动力学约束的引导下，动态平衡目标跟踪与安全恢复之间的权衡。为评估HWC-Loco的性能，我们与当前最先进的人形机器人控制模型开展了大量对比实验，结果表明，在仿真与真实环境下，HWC-Loco在多样地形、机器人结构及运动任务中均表现出更优性能。
英文摘要：
Humanoid robots, capable of assuming human roles in various workplaces, have become essential to embodied intelligence. However, as robots with complex physical structures, learning a control model that can operate robustly across diverse environments remains inherently challenging, particularly under the discrepancies between training and deployment environments. In this study, we propose HWC-Loco, a robust whole-body control algorithm tailored for humanoid locomotion tasks. By reformulating policy learning as a robust optimization problem, HWC-Loco explicitly learns to recover from safety-critical scenarios. While prioritizing safety guarantees, overly conservative behavior can compromise the robot's ability to complete the given tasks. To tackle this challenge, HWC-Loco leverages a hierarchical policy for robust control. This policy can dynamically resolve the trade-off between goal-tracking and safety recovery, guided by human behavior norms and dynamic constraints. To evaluate the performance of HWC-Loco, we conduct extensive comparisons against state-of-the-art humanoid control models, demonstrating HWC-Loco's superior performance across diverse terrains, robot structures, and locomotion tasks under both simulated and real-world environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
