脳に着想を得たゼロショットロボットタスク推論と実行のための階層的フレームワーク
A Brain-inspired Hierarchical Framework for Zero-Shot Robot Task Reasoning and Execution
オープンな言語指示に従うロボットのための、脳の役割分担に着想を得たゼロショット階層的フレームワークを提案し、視覚認識、言語接地、コストベースの計画、実行検証を統合して、長期的なタスク分解と物理的検証を実現した。
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著者: Guangming Wang, Pengfei Ye, Qizhen Ying, Yixiong Jing, Yuxiang Ma, Haonan Chen, Haibing Wu, Olaf Wysocki, Molong Duan, Brian Sheil
分類: cs.RO
原文アブストラクト
Robots that follow open-ended language instructions need to connect semantic intent to visual scene understanding, geometric feasibility, object states, and physical interaction conditions. End-to-end Vision-Language-Action policies have improved cross-task generalization, but they typically map visual and language inputs directly to robot actions, leaving limited explicit structure for long-horizon decomposition, physical verification, and recovery. We present \method, a zero-shot hierarchical framework functionally inspired by the division of roles in the human brain, comprising visual perception and state inference, language grounding and action-sequence generation from a shared atomic action library, cost-based plan selection, and real-robot execution and verification. The framework grounds commands in explicit object states, composes reusable atomic actions into task-conditioned sequences, ranks alternative sequences by execution cost, and verifies intermediate physical outcomes from refreshed observations. In the evaluation, \method{} completes 10/10 clean board trials, 10/10 pick-and-place trials, and 4/5 pyramid stacking trials for both the flat and irregular initial-layout conditions; the corresponding mean task progress is $99.03\%$, $100.00\%$, and $96.67\%$ respectively. Across all evaluated conditions, \method{} achieves higher success rates than ReKep, Dream2Flow, and $π_{0.5}$ benchmarks, demonstrating the effectiveness of combining explicit object-state reasoning, compositional atomic actions, cost-based plan selection, and closed-loop execution verification.
