Feasibility-Guided Planning over Multi-Specialized Locomotion Policies

2026-02-08

AI Summary

This work proposes a feasibility-guided path planning framework to address the challenge of coordinating multiple specialized locomotion strategies over unstructured terrain. The approach equips each terrain-specific strategy with a lightweight Feasibility-Net that predicts a feasibility tensor from local elevation maps and task vectors, thereby guiding classical planning algorithms to generate optimal paths consistent with the capabilities of the selected strategy. The framework supports plug-and-play integration of new strategies without retraining, while preserving both interpretability and strategy consistency. Experimental results in both simulation and real-world environments demonstrate that the method efficiently produces reliable paths and significantly enhances adaptability to complex and diverse terrains.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• legged robotics
• multi-specialized locomotion
• feasibility-guided planning
• unstructured terrain
• policy integration

Innovation

• feasibility-guided planning
• multi-specialized locomotion
• Feasibility-Net
• legged robotics
• terrain-aware planning