用学习到的符号规则快速规划，再用连续模型验证，兼顾效率与准确。
Bilevel Planning with Learned Symbolic Abstractions from Interaction Data
- 高层用学习的符号规则生成候选计划，低层用连续模型验证并搜索
- 在多物体操作任务中，成功率接近连续搜索，但耗时大幅降低
- 适合需要高效且可靠规划的复杂环境任务
智能体在复杂环境中需同时处理连续动态和离散表示以生成有效计划。以往方法虽能从机器人无监督探索中学习符号抽象，但依赖确定性符号域，缺乏对生成计划的验证机制，且仅在抽象层面运行，难以捕捉环境的连续动态。为此，我们提出一种双层神经符号框架：学习得到的概率符号规则在高层快速生成候选计划，而学习的连续效应模型在低层负责验证计划并必要时执行前向搜索。在多物体操作任务上的实验表明，该方法优于纯符号方法，能可靠识别失败计划，其规划性能在统计上与连续前向搜索相当，同时多数问题通过高效的符号推理解决。
原文摘要 · Abstract (English)
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.
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