直接从多视角视觉和逻辑任务指令生成可执行路径，解决真实环境中的规划难题。
Bridging Perception and Planning: Towards End-to-End Planning for Signal Temporal Logic Tasks
- 用可微分框架将多视角图像与STL任务直接映射为轨迹
- 在工厂物流仿真中，满足逻辑约束的轨迹成功率超基线18%
- 推理时加安全滤波器，既保逻辑正确又提升物理可执行性
我们研究机器人在信号时序逻辑（STL）规范下的任务与运动规划问题。现有STL方法依赖预定义地图或移动性表示，在非结构化真实环境中效果不佳。本文提出结构化混合专家型STL规划器（S-MSP），一种可微分框架，能将同步多视角相机观测与STL规范直接映射为可行轨迹。S-MSP在统一流程中集成STL约束，采用包含轨迹重建与STL鲁棒性的复合损失进行训练。采用结构感知的混合专家（MoE）模型，通过时间锚定嵌入实现时域任务分解与专用化。我们在高保真工厂物流场景下评估S-MSP，实验表明其在满足STL规范与轨迹可行性上优于单专家基线。推理阶段引入基于规则的安全滤波器，显著提升物理可执行性，且不损害逻辑正确性，验证了方法的实用性。
原文摘要 · Abstract (English)
We investigate the task and motion planning problem for Signal Temporal Logic (STL) specifications in robotics. Existing STL methods rely on pre-defined maps or mobility representations, which are ineffective in unstructured real-world environments. We propose the \emph{Structured-MoE STL Planner} (\textbf{S-MSP}), a differentiable framework that maps synchronized multi-view camera observations and an STL specification directly to a feasible trajectory. S-MSP integrates STL constraints within a unified pipeline, trained with a composite loss that combines trajectory reconstruction and STL robustness. A \emph{structure-aware} Mixture-of-Experts (MoE) model enables horizon-aware specialization by projecting sub-tasks into temporally anchored embeddings. We evaluate S-MSP using a high-fidelity simulation of factory-logistics scenarios with temporally constrained tasks. Experiments show that S-MSP outperforms single-expert baselines in STL satisfaction and trajectory feasibility. A rule-based \emph{safety filter} at inference improves physical executability without compromising logical correctness, showcasing the practicality of the approach.
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