知覚タスク計画のための完全微分可能なニューロ・ソフト・シンボリックフレームワーク
A Fully Differentiable Neuro-Soft-Symbolic Framework for Perceptual Task Planning
視覚知覚とタスク計画を単一の計算グラフで結び、微分可能なソフトシンボリック状態と遷移演算子で計画を最適化する手法を提案。不確実な知覚下でも高い成功率を達成し、ロボット動作実行との整合性も検証した。
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著者: Hongyan Wei, Wael AbdAlmageed
分類: cs.AI, cs.RO
原文アブストラクト
Perceptual planning tasks require two key capabilities: accurately perceiving uncertain scenes and planning valid action sequences following logical rules. Conventional methods convert perception into discrete symbolic facts and then plan, discarding perceptual uncertainty and severing task-level feedback to perception. We introduce a generic, fully differentiable neuro-soft-symbolic framework that connects visual perception and task planning within a single computational graph. The framework maintains a continuous soft symbolic state, lifts domain rules into a differentiable soft-$T_P$ transition operator, and optimizes action logits over a short planning horizon. Gradients from the planning objective can also update the perception parameters, allowing task-relevant perceptual representations to be refined during planning. On Blocksworld, our method solves 40/40 LatPlan-40 tasks and 596/600 PlanBench-600 tasks, compared with 33/40 for LatPlan and 587/600 for the reasoning-model baseline, while requiring substantially less computation and time. In the perceptual-uncertainty ablation, our method improves the success rate from 59\% with frozen perception to 83\%. We further conduct task-and-motion simulations on Blocksworld scenes, providing an execution-level validation of the compatibility between decoded task plans and downstream robotic motion execution.