This paper presents TSPDiffuser, a novel data-driven path planner for
traveling salesperson path planning problems (TSPPPs) in environments rich with
obstacles. Given a set of destinations within obstacle maps, our objective is
to efficiently find the shortest possible collision-free path that visits all
the destinations. In TSPDiffuser, we train a diffusion model on a large
collection of TSPPP instances and their respective solutions to generate
plausible paths for unseen problem instances. The model can then be employed as
a learned sampler to construct a roadmap that contains potential solutions with
a small number of nodes and edges. This approach enables efficient and accurate
estimation of traveling costs between destinations, effectively addressing the
primary computational challenge in solving TSPPPs. Experimental evaluations
with diverse synthetic and real-world indoor/outdoor environments demonstrate
the effectiveness of TSPDiffuser over existing methods in terms of the
trade-off between solution quality and computational time requirements.

该论文介绍了一种新颖的基于数据驱动的路径规划器 TSPDiffuser，用于处理充满障碍物的旅行推销员路径规划问题（TSPPPs）。通过在大量 TSPPP 实例及其解决方案上训练扩散模型，TSPDiffuser 可以生成未见问题实例的可行路径，进而构建包含少量节点和边的路网，高效准确地估计目的地之间的旅行代价，从而有效解决 TSPPPs 中的主要计算挑战。与现有方法相比，通过在各种合成和实际室内 / 室外环境中进行实验评估，TSPDiffuser 在解决质量和计算时间需求之间取得了良好的平衡。

TSPDiffuser: 旅行推销员路径规划问题的学习取样扩散模型

TSPDiffuser: Diffusion Models as Learned Samplers for Traveling  Salesperson Path Planning Problems

Grounding language to the visual observations of a navigating agent can be
performed using off-the-shelf visual-language models pretrained on
Internet-scale data (e.g., image captions). While this is useful for matching
images to natural language descriptions of object goals, it remains disjoint
from the process of mapping the environment, so that it lacks the spatial
precision of classic geometric maps. To address this problem, we propose
VLMaps, a spatial map representation that directly fuses pretrained
visual-language features with a 3D reconstruction of the physical world. VLMaps
can be autonomously built from video feed on robots using standard exploration
approaches and enables natural language indexing of the map without additional
labeled data. Specifically, when combined with large language models (LLMs),
VLMaps can be used to (i) translate natural language commands into a sequence
of open-vocabulary navigation goals (which, beyond prior work, can be spatial
by construction, e.g., "in between the sofa and TV" or "three meters to the
right of the chair") directly localized in the map, and (ii) can be shared
among multiple robots with different embodiments to generate new obstacle maps
on-the-fly (by using a list of obstacle categories). Extensive experiments
carried out in simulated and real world environments show that VLMaps enable
navigation according to more complex language instructions than existing
methods. Videos are available at this https URL.

本研究提出了 VLMaps，一种将 3D 物理世界重建与预训练的视觉语言特征直接融合的空间地图表示，其能够通过大型语言模型将自然语言命令直接转化为空间导航目标。VLMaps 能够在不需要额外标注数据的情况下自主构建，并且可以与多种类型的机器人共享，能够进行导航的同时生成新的障碍物地图。实验表明，VLMaps 可以支持人类语言含义更丰富的导航指令。