释放轮腿机器人在高动态反射性障碍规避中的敏捷性
Unleashing the Agility of Wheeled-Legged Robots for High-Dynamic Reflexive Obstacle Evasion
AI总结：
本文提出AWARE框架，通过层次强化学习实现轮腿机器人在动态环境中的敏捷避障，展现多样化的运动模式和规避行为，验证了轮腿机器人在高动态威胁下的敏捷性。
摘要：
轮腿机器人结合了轮式运动的能效和腿部系统的地形适应性，使其成为复杂动态环境中敏捷移动的有前景平台。然而，由于混合形态、模式耦合和非holonomic约束，实现高动态反射性避障仍具挑战性。本文提出AWARE，即自适应轮腿避障和反射性规避，是一种用于轮腿机器人高动态障碍规避的分层强化学习框架。所提系统自然展现出多样化的涌现步态和规避行为，包括前冲和侧闪，从而利用机器人的混合形态来增强在高动态威胁下的敏捷性。在Isaac Lab模拟和M20平台上的广泛实验表明，AWARE在多样动态场景中实现了稳健和敏捷的障碍规避，揭示了行为上不同的规避策略。这些结果突显了AWARE的实用有效性以及轮腿机器人内在的反射敏捷性。
英文摘要：
Wheeled-legged robots combine the efficiency of rolling with the adaptability of legged locomotion, offering unique agility for dynamic environments. However, enabling rapid reflexive evasion remains challenging due to the coexistence of heterogeneous wheel-leg dynamics, hybrid locomotion modes, and non-holonomic constraints. In this work, we investigate how wheeled-legged robots can exploit their hybrid morphology for high-dynamic obstacle avoidance. We propose AWARE, a hierarchical reinforcement learning framework that decomposes avoidance into navigation-avoidance and reflexive-evasion regimes coordinated by a threat-conditioned high-level policy. By learning specialized low-level experts, AWARE autonomously discovers distinct rolling-, stepping-, and hybrid-dominated evasive behaviors, including forward lunges and lateral dodges. Simulation experiments across different reaction times and approach directions, together with real-robot evaluations on the M20 platform, demonstrate improved evasion capability and effective online transition between locomotion regimes. These results highlight the potential of exploiting hybrid wheel-leg actuation for agile and reactive mobility in dynamic environments. Paper homepage: https://aware-ral-2026.github.io/.
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。
