We present TANGO (Tensor ANd Graph Optimization), a novel motion planning framework that integrates tensor-based compression with structured graph optimization to enable efficient and scalable trajectory generation. While optimization-based planners such as the Graph of Convex Sets (GCS) offer powerful tools for generating smooth, optimal trajectories, they typically rely on a predefined convex characterization of the high-dimensional configuration space-a requirement that is often intractable for general robotic tasks. TANGO builds further by using Tensor Train decomposition to approximate the feasible configuration space in a compressed form, enabling rapid discovery and estimation of task-relevant regions. These regions are then embedded into a GCS-like structure, allowing for geometry-aware motion planning that respects both system constraints and environmental complexity. By coupling tensor-based compression with structured graph reasoning, TANGO enables efficient, geometry-aware motion planning and lays the groundwork for more expressive and scalable representations of configuration space in future robotic systems. Rigorous simulation studies on planar and real robots reinforce our claims of effective compression and higher quality trajectories.

翻译：本文提出TANGO（张量与图优化）——一种集成张量压缩与结构化图优化的新型运动规划框架，能够实现高效可扩展的轨迹生成。虽然基于优化的规划器（如凸集图GCS）为生成平滑最优轨迹提供了强大工具，但它们通常依赖于对高维构型空间的预定义凸表征，这一要求对于通用机器人任务往往难以实现。TANGO通过张量列车分解以压缩形式近似可行构型空间，实现了任务相关区域的快速发现与估计。这些区域随后被嵌入类GCS结构中，从而支持同时满足系统约束与环境复杂度的几何感知运动规划。通过耦合张量压缩与结构化图推理，TANGO实现了高效的几何感知运动规划，并为未来机器人系统中构型空间的更具表现力与可扩展性的表征奠定了基础。在平面机器人与真实机器人上的严格仿真研究验证了其在有效压缩与高质量轨迹生成方面的优势。