A Brain-inspired Hierarchical Framework for Zero-Shot Robot Task Reasoning and Execution

• URL: http://arxiv.org/abs/2609.05985v1
• Date: Sat, 05 Sep 2026 08:59:04 GMT
• Title: A Brain-inspired Hierarchical Framework for Zero-Shot Robot Task Reasoning and Execution
• Abstract summary: Method is a zero-shot hierarchical framework inspired by the division of roles in the human brain.<n>It grounds commands in explicit object states, composes reusable atomic actions into task-conditioned sequences, and ranks alternative sequences by execution cost.<n>Method achieves higher success rates than ReKep, Dream2Flow, and $_0.5$ benchmarks.
• Score: 9.745144598922584
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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