Unleashing the Agility of Wheeled-Legged Robots for High-Dynamic Reflexive Obstacle Evasion

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. Evaluations in Isaac Lab and on the physical M20 platform show that AWARE enables short-reaction-time reflexive evasion and elicits behaviorally distinct strategies. These results demonstrate the effectiveness of the proposed framework in the evaluated settings and illustrate how the wheeled-legged morphology can support distinct evasive behaviors.