用符号规划与神经网络结合，让机器人更聪明地完成复杂任务。
Hierarchical Neuro-Symbolic Decision Transformer
- 高层用符号逻辑规划任务步骤，底层用Transformer生成具体动作。
- 在随机网格世界中成功率和效率均优于纯符号、纯神经网络方法。
- 适合需要可解释性与适应性的长期决策任务，如机器人控制。
我们提出一种分层神经符号控制框架，将经典符号规划器与基于Transformer的策略紧密结合，以应对不确定性下的长时程决策问题。高层规划器生成符合任务约束的可解释操作序列，低层将每个操作转化为子目标标记，由决策Transformer直接根据原始观测生成细粒度动作。这种双向接口在保持符号推理的组合效率与可解释性的同时，不牺牲深度序列模型的适应性，并支持对规划与执行中近似误差在层级间累积的严谨分析。在随机网格世界中的实证研究显示，该方法在成功率与效率上持续超越纯符号、纯神经及现有分层基线，展现出在序列任务中的鲁棒性。
原文摘要 · Abstract (English)
We present a hierarchical neuro-symbolic control framework that tightly couples a classical symbolic planner with a transformer-based policy to address long-horizon decision-making under uncertainty. At the high level, the planner assembles an interpretable sequence of operators that guarantees logical coherence with task constraints, while at the low level each operator is rendered as a sub-goal token that conditions a decision transformer to generate fine-grained actions directly from raw observations. This bidirectional interface preserves the combinatorial efficiency and explainability of symbolic reasoning without sacrificing the adaptability of deep sequence models, and it permits a principled analysis that tracks how approximation errors from both planning and execution accumulate across the hierarchy. Empirical studies in stochastic grid-world domains demonstrate that the proposed method consistently surpasses purely symbolic, purely neural and existing hierarchical baselines in both success and efficiency, highlighting its robustness for sequential tasks.
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