A Fully Differentiable Neuro-Soft-Symbolic Framework for Perceptual Task Planning

2026-09-17

AI Summary

本文提出一种全可微神经软符号框架，解决感知任务规划中不确定性问题，通过连接视觉感知与任务规划，优化行动序列，提高任务成功率。

Abstract

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.

Problem

• Perceptual Planning
• Uncertainty
• Logical Rules

Innovation

• fully differentiable
• neuro-soft-symbolic framework
• perceptual task planning
• continuous soft symbolic state
• differentiable soft-$T_P$ transition operator