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The paper introduces Structured-MoE STL Planner (S-MSP), a differentiable framework for end-to-end task and motion planning from multi-view camera observations and STL specifications. S-MSP integrates STL constraints directly into the training loop using a composite loss function that combines trajectory reconstruction and STL robustness. The core innovation is a structure-aware Mixture-of-Experts (MoE) model that enables horizon-aware specialization by projecting sub-tasks into temporally anchored embeddings, leading to improved STL satisfaction and trajectory feasibility in factory-logistics scenarios.
Skip the hand-engineered maps: this end-to-end framework learns to plan robot trajectories directly from camera feeds and temporal logic constraints.
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.
Tsinghua AI