外观
论文arXiv 2610.2012114 天前
HM3D-M3：面向多智能体、多模态、多对象目标导航的基准
原文链接：HM3D-M3: A Benchmark for Multi-Agent, Multi-Modal, Multi-Object Goal Navigation（arXiv 2610.20121 · 发布日期：2026-10-14）
HM3D-M3: A Benchmark for Multi-Agent, Multi-Modal, Multi-Object Goal Navigation
HM3D-M3针对多智能体多模态多对象导航，构建基准并以两种基线在室内场景和三机器人团队上验证。
核心要点
- 现有基准分别研究多智能体、多模态和多对象导航，缺少在同一回合中同时覆盖三者的评测。
- HM3D-M3中，K个智能体从分布位置出发，寻找以类别、图像或描述给出的M个无序对象目标。
- 基准提供1000个验证回合、3926个目标、100K个训练样本，并提出目标级SR和归一化SPL指标。
- 作者贡献HiGA和LaViRA-M3两种基线，SR达38--41%，并部署LaViRA-M3到异构三机器人团队。
验证范围与局限：验证范围包括照片级真实室内场景中的基准评测，以及LaViRA-M3在异构三机器人团队上的部署。
英文摘要 (Abstract)
Rapid progress in AI agents and robotics makes it a matter of the near future that humans are served by a team of robots. Such a team will receive several goals at once, each specified in whatever form is convenient for the human, such as a category name, a photo, or a spoken description. Existing benchmarks study multi-agent, multi-modal, and multi-object goal navigation in isolation, and a benchmark that places all three in one episode is missing. To fill this gap, we introduce HM3D-M3. A team of K agents starts at distributed locations in a photorealistic indoor scene and searches for an unordered set of M object goals, each given as an open-vocabulary category, an instance image, or an instance description. HM3D-M3 provides 1,000 validation episodes over 3,926 goals from 379 open-vocabulary categories, 100K observation-assignment training samples, and two team-level metrics, goal-level SR and makespan-normalized SPL. We further contribute two baselines. HiGA is a trained hierarchical goal assigner on top of a frozen open-vocabulary navigation policy, and LaViRA-M3 is a training-free team of VLM agents with shared mapping and progress analysis. Adapted multi-robot and zero-shot methods reach 16--27% SR, while our baselines reach 38--41% with complementary strengths and distinct failure modes. Furthermore, we deploy LaViRA-M3 on a heterogeneous three-robot team. Project page: https://robo-lavira.github.io/hm3d-m3/
作者：Hongyu Ding, Kanyu Xu, Mingjia Chen, Zixuan Chen, Jieqi Shi, Jing Huo, Yifan Zhang, Yang Gao
来源
本页事实对应的公开页面，以原文为准。