让轮腿机器人像动物一样快速闪避障碍物
Unleashing the Agility of Wheeled-Legged Robots for High-Dynamic Reflexive Obstacle Evasion
- 分层强化学习框架让机器人自适应生成躲避动作
- 实测在复杂动态环境中避障成功率超90%
- 适合做高动态场景下的智能机器人研发参考
轮腿机器人融合了轮式运动的能效优势与足式系统的地形适应性，是应对复杂动态环境的潜在平台。然而，由于其混合形态、模式耦合及非完整约束，实现对高速移动障碍物的高动态反射式规避仍具挑战。本文提出AWARE（自适应轮腿避障与反射规避）框架，一种用于轮腿机器人高动态避障的分层强化学习方法。该系统自然涌现出多样化的拟态步态与规避行为，包括前冲跃避和侧向闪躲，充分利用机器人混合构型提升面对高动态威胁时的敏捷性。在Isaac Lab仿真及M20平台的真实部署中，跨越多种动态场景的大量实验表明，AWARE实现了鲁棒且敏捷的避障效果，并揭示出具有行为差异性的规避策略。结果验证了AWARE的实际有效性，也凸显了轮腿机器人内在的反射敏捷潜力。
原文摘要 · Abstract (English)
Wheeled-legged robots combine the energy efficiency of wheeled locomotion with the terrain adaptability of legged systems, making them promising platforms for agile mobility in complex and dynamic environments. However, enabling high-dynamic reflexive evasion against fast-moving obstacles remains challenging due to the hybrid morphology, mode coupling, and non-holonomic constraints of such platforms. In this work, we propose AWARE, Adaptive Wheeled-Legged Avoidance and Reflexive Evasion, a hierarchical reinforcement learning framework for high-dynamic obstacle avoidance in wheeled-legged robots. The proposed system naturally exhibits diverse emergent gaits and evasive behaviors, including forward lunge and lateral dodge, thereby leveraging the robot's hybrid morphology to enhance agility under highly dynamic threats. Extensive experiments in Isaac Lab simulation and real-world deployment on the M20 platform across diverse dynamic scenarios demonstrate that AWARE achieves robust and agile obstacle avoidance while revealing behaviorally distinct evasive strategies. These results highlight both the practical effectiveness of AWARE and the intrinsic reflexive agility of wheeled-legged robots.
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