让机器人在复杂地形中智能规划路径，自动匹配最合适的行走策略。
Feasibility-Guided Planning over Multi-Specialized Locomotion Policies
- 为每种地形策略配备可行性预测网络，实时判断是否适用。
- 在仿真与真实场景中均实现稳定可靠路径规划，适应多种挑战性地形。
- 无需重新训练即可新增行走策略，适合需要灵活适应的机器人系统。
在非结构化地形上进行规划是足式机器人领域的重大挑战。尽管强化学习已生成多种运动策略，但如何对多个专家策略进行有效规划仍存在难题。现有方法受限于：传统规划器无法融合特定技能策略，而分层学习框架常丧失可解释性，且新增策略时需重新训练。本文提出一种可行性引导的规划框架，成功集成多个地形特异性策略。每个策略均搭配一个可行性网络（Feasibility-Net），基于局部高程图与任务向量学习预测可行性张量。该机制使经典规划算法能够生成最优路径。通过仿真与真实实验验证，本方法在多样化、复杂地形下均能高效生成可靠路径，且始终与底层策略能力保持一致。
原文摘要 · Abstract (English)
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.
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