Parkour tasks for quadrupeds have emerged as a promising benchmark for agile locomotion. While human athletes can effectively perceive environmental characteristics to select appropriate footholds for obstacle traversal, endowing legged robots with similar perceptual reasoning remains a significant challenge. Existing methods often rely on hierarchical controllers that follow pre-computed footholds, thereby constraining the robot's real-time adaptability and the exploratory potential of reinforcement learning. To overcome these challenges, we present PUMA, an end-to-end learning framework that integrates visual perception and foothold priors into a single-stage training process. This approach leverages terrain features to estimate egocentric polar foothold priors, composed of relative distance and heading, guiding the robot in active posture adaptation for parkour tasks. Extensive experiments conducted in simulation and real-world environments across various discrete complex terrains, demonstrate PUMA's exceptional agility and robustness in challenging scenarios.

翻译：四足机器人跑酷任务已成为敏捷运动能力的重要基准。虽然人类运动员能够有效感知环境特征以选择适当的落脚点来穿越障碍，但赋予腿式机器人类似的感知推理能力仍是一个重大挑战。现有方法通常依赖于遵循预计算落脚点的分层控制器，从而限制了机器人的实时适应性和强化学习的探索潜力。为克服这些挑战，我们提出了PUMA，一种将视觉感知与落脚点先验整合到单阶段训练过程中的端到端学习框架。该方法利用地形特征来估计以自我为中心的极坐标落脚点先验（由相对距离和航向角组成），引导机器人在跑酷任务中进行主动姿态调整。在仿真和真实环境中对各种离散复杂地形进行的大量实验表明，PUMA在挑战性场景中展现出卓越的敏捷性和鲁棒性。