Planning over unstructured terrain presents a significant challenge in the field of legged robotics. Although recent works in reinforcement learning have yielded various locomotion strategies, planning over multiple experts remains a complex issue. Existing approaches encounter several constraints: traditional planners are unable to integrate skill-specific policies, whereas hierarchical learning frameworks often lose interpretability and require retraining whenever new policies are added. In this paper, we propose a feasibility-guided planning framework that successfully incorporates multiple terrain-specific policies. Each policy is paired with a Feasibility-Net, which learned to predict feasibility tensors based on the local elevation maps and task vectors. This integration allows classical planning algorithms to derive optimal paths. Through both simulated and real-world experiments, we demonstrate that our method efficiently generates reliable plans across diverse and challenging terrains, while consistently aligning with the capabilities of the underlying policies.

翻译：在足式机器人领域，非结构化地形规划是一个重大挑战。尽管近期强化学习研究已产生多种运动策略，但在多个专家策略上进行规划仍是一个复杂问题。现有方法面临若干限制：传统规划器无法整合技能专用策略，而分层学习框架往往丧失可解释性，且每当新增策略时都需要重新训练。本文提出一种可行性指导的规划框架，成功整合了多种地形专用策略。每个策略都配有一个可行性网络（Feasibility-Net），该网络通过学习基于局部高程图和任务向量来预测可行性张量。这种整合使得经典规划算法能够推导出最优路径。通过仿真和真实世界实验，我们证明该方法能在多样且具有挑战性的地形上高效生成可靠规划，同时始终与底层策略的能力保持一致。