タスクレベル意思決定から歩容制御へ：四脚ナビゲーションのための階層型ポリシーアプローチ
Task-Level Decisions to Gait Level Control: A Hierarchical Policy Approach for Quadruped Navigation
四脚ロボットのナビゲーションにおいて、高レベルのタスク意思決定と低レベルの歩容実行のスケール不整合を解決するため、階層型ポリシーアーキテクチャを提案し、シミュレーションから実機への転送を強化した。
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著者: Sijia Li, Haoyu Wang, Shenghai Yuan, Yizhuo Yang, Thien-Minh Nguyen
分類: cs.RO
原文アブストラクト
Real-world quadruped navigation is constrained by a scale mismatch between high-level navigation decisions and low-level gait execution, as well as by instabilities under out-of-distribution environmental changes. Such variations challenge sim-to-real transfer and can trigger falls when policies lack explicit interfaces for adaptation. In this paper, we present a hierarchical policy architecture for quadrupedal navigation, termed Task-level Decision to Gait Control (TDGC). A low-level policy, trained with reinforcement learning in simulation, delivers gait-conditioned locomotion and maps task requirements to a compact set of controllable behavior parameters, enabling robust mode generation and smooth switching. A high-level policy makes task-centric decisions from sparse semantic or geometric terrain cues and translates them into low-level targets, forming a traceable decision pipeline without dense maps or high-resolution terrain reconstruction. Different from end-to-end approaches, our architecture provides explicit interfaces for deployment-time tuning, fault diagnosis, and policy refinement. We introduce a structured curriculum with performance-driven progression that expands environmental difficulty and disturbance ranges. Experiments show higher task success rates on mixed terrains and out-of-distribution tests.
