多专业运动策略的可行性引导规划
Feasibility-Guided Planning over Multi-Specialized Locomotion Policies
AI总结：
本文提出一种可行性引导的规划框架，整合多个地形特定策略，通过Feasibility-Net预测可行性张量，实现高效可靠的路径规划。
摘要：
在腿部机器人领域，无结构地形的规划是一个重大挑战。尽管最近强化学习的研究已经产生了多种运动策略，但规划多个专家仍是一个复杂的问题。现有方法面临几个限制：传统规划器无法整合技能特定的策略，而分层学习框架往往失去可解释性，并且每当添加新策略时都需要重新训练。在本文中，我们提出了一种可行性引导的规划框架，成功整合了多个地形特定的策略。每个策略都配有一个Feasibility-Net，该网络学习根据局部高程地图和任务向量预测可行性张量。这种整合使经典规划算法能够推导出最优路径。通过模拟和现实世界实验，我们证明了我们的方法在多样且具有挑战性的地形上能够高效生成可靠计划，同时始终与底层策略的能力保持一致。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
