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.
@article{arxiv.2602.07932,
title = {Feasibility-Guided Planning over Multi-Specialized Locomotion Policies},
author = {Ying-Sheng Luo and Lu-Ching Wang and Hanjaya Mandala and Yu-Lun Chou and Guilherme Christmann and Yu-Chung Chen and Yung-Shun Chan and Chun-Yi Lee and Wei-Chao Chen},
journal= {arXiv preprint arXiv:2602.07932},
year = {2026}
}