让四足机器人像人一样看环境选落脚点，实现敏捷越障。
PUMA: Perception-driven Unified Foothold Prior for Mobility Augmented Quadruped Parkour
- 用视觉感知直接生成落脚点先验，端到端训练。
- 在多种复杂地形上实现实时自适应与稳定越障。
- 适合研究机器人自主导航与强化学习的学者。
四足机器人越障任务已成为敏捷运动的重要基准。人类运动员能有效感知环境特征并选择合适落脚点以跨越障碍，但赋予仿生机器人类似的感知推理能力仍面临重大挑战。现有方法多依赖分层控制器配合预设落脚点，限制了机器人实时适应性和强化学习的探索潜力。为此，我们提出PUMA，一种将视觉感知与落脚点先验融合的端到端学习框架。该方法利用地形特征估计以自身为中心的极坐标落脚点先验（相对距离与方向），指导机器人主动调整姿态完成越障任务。在多种离散复杂地形的仿真与真实环境实验中，PUMA展现出卓越的敏捷性与鲁棒性，验证了其在挑战性场景下的有效性。
原文摘要 · Abstract (English)
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.
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