Intelligent agents must reason over both continuous dynamics and discrete representations to generate effective plans in complex environments. Previous studies have shown that symbolic abstractions can emerge from neural effect predictors trained with a robot's unsupervised exploration. However, these methods rely on deterministic symbolic domains, lack mechanisms to verify the generated symbolic plans, and operate only at the abstract level, often failing to capture the continuous dynamics of the environment. To overcome these limitations, we propose a bilevel neuro-symbolic framework in which learned probabilistic symbolic rules generate candidate plans rapidly at the high level, and learned continuous effect models verify these plans and perform forward search when necessary at the low level. Our experiments on multi-object manipulation tasks demonstrate that the proposed bilevel method outperforms symbolic-only approaches, reliably identifying failing plans through verification, and achieves planning performance statistically comparable to continuous forward search while resolving most problems via efficient symbolic reasoning.

翻译：智能体必须在复杂环境中同时推理连续动态与离散表示，以生成有效规划。先前研究表明，符号抽象可通过机器人无监督探索训练的神经效应预测器自然涌现。然而，这些方法依赖确定性符号域，缺乏验证生成符号规划的机制，且仅在抽象层面运行，往往难以捕捉环境的连续动态特性。为克服这些局限，我们提出一种双层神经符号框架：在高层通过学习的概率符号规则快速生成候选规划，在底层通过学习的连续效应模型验证这些规划并在必要时执行前向搜索。我们在多物体操控任务上的实验表明，所提出的双层方法优于纯符号方法，能通过验证可靠识别失败规划，其规划性能在统计意义上与连续前向搜索相当，同时通过高效符号推理解决了大多数问题。