Bridging continuous perceptual signals and discrete symbolic reasoning is a fundamental challenge in AI systems that must operate under uncertainty. We present a neuro-symbolic framework that explicitly models and propagates uncertainty from perception to planning, providing a principled connection between these two abstraction levels. Our approach couples a transformer-based perceptual front-end with graph neural network (GNN) relational reasoning to extract probabilistic symbolic states from visual observations, and an uncertainty-aware symbolic planner that actively gathers information when confidence is low. We demonstrate the framework's effectiveness on tabletop robotic manipulation as a concrete application: the translator processes 10,047 PyBullet-generated scenes (3--10 objects) and outputs probabilistic predicates with calibrated confidences (overall F1=0.68). When embedded in the planner, the system achieves 94\%/90\%/88\% success on Simple Stack, Deep Stack, and Clear+Stack benchmarks (90.7\% average), exceeding the strongest POMDP baseline by 10--14 points while planning within 15\,ms. A probabilistic graphical-model analysis establishes a quantitative link between calibrated uncertainty and planning convergence, providing theoretical guarantees that are validated empirically. The framework is general-purpose and can be applied to any domain requiring uncertainty-aware reasoning from perceptual input to symbolic planning.

翻译：连接连续感知信号与离散符号推理是人工智能系统在不确定性环境下运行所面临的基础性挑战。本文提出一种神经符号框架，该框架明确建模并将不确定性从感知层传播至规划层，为这两个抽象层次之间建立了原则性连接。我们的方法将基于Transformer的感知前端与图神经网络（GNN）关系推理相结合，从视觉观测中提取概率性符号状态，并采用一个不确定性感知的符号规划器，在置信度较低时主动收集信息。我们以桌面机器人操作为具体应用场景验证了该框架的有效性：翻译器处理了10,047个PyBullet生成的场景（包含3-10个物体），输出具有校准置信度的概率谓词（整体F1=0.68）。当嵌入规划器时，该系统在Simple Stack、Deep Stack和Clear+Stack基准测试中分别达到94%/90%/88%的成功率（平均90.7%），在15毫秒内完成规划的同时，比最强的POMDP基线高出10-14个百分点。通过概率图模型分析，我们建立了校准不确定性规划收敛之间的定量联系，提供了理论保证并得到实证验证。该框架具有通用性，可应用于任何需要从感知输入到符号规划进行不确定性感知推理的领域。