用二分查找定位机器人路径上的目标位置，开源方案媲美闭源模型。
Binary Tracking for Spatial QA and Navigation with Open Vision-Language Models
- 基于轨迹时间顺序，对查询中的地标间段进行二分搜索定位。
- 在SpaceLocQA上准确率提升22.8%，接近GPT-4o表现。
- 推理速度超前方法1.5倍，适合嵌入式机器人实时部署。
本文针对服务机器人在长距离第一人称路径中进行空间问答的需求，解决如“回家路上哪里有干洗店？”这类问题，返回可被导航系统使用的度量坐标。现有方法依赖GPT-4o等闭源模型构建检索增强型代理，但实际部署中受网络不稳定、通信延迟和成本限制，难以依赖。为此提出完全开源的BinTrack系统，利用机器人轨迹的时间顺序，在查询指定的两个锚点地标之间执行二分搜索定位。该方法在SpaceLocQA基准的全局类别上准确率提升达22.8%，甚至与使用GPT-4o的闭源模型结果相当。同时，其优化的推理策略使推理速度超过此前方法1.5倍以上。此外，本文发布新数据集GangnamLoop，由真实四足机器人在公共街道上多轮采集，包含不同户外条件下的同一地点，配以机器人低视角与人类主人视角数据，支持更真实的场景评估。代码与数据已公开于https://github.com/ndb796/BinaryTracking。
原文摘要 · Abstract (English)
This work addresses spatial question answering for service robots traversing long egocentric routes. Given a query such as "where can I find a dry cleaner on the way back home?", the system returns a metric coordinate that downstream navigation components can act on. Prior Spatial Question Answering approaches leverage retrieval-augmented agents built on closed-source models such as GPT-4o for path exploration. However, robots operating in the real world often cannot reliably depend on online closed-source models due to network instability, communication latency, and deployment cost. It creates a need for open-source based Spatial Question Answering approaches that can run onboard the robot, yet prior research in this direction remains limited. This work proposes BinTrack, a simple yet effective, fully open-source spatial-localization agent that leverages the temporal ordering of a robot's trajectory. BinTrack performs a binary search over the trajectory segments between two anchor landmarks identified from a query. It improves overall accuracy by up to 22.8% over other open-source implementations and even matches the reported closed-source model result on the global category of the SpaceLocQA benchmark, the most challenging setting that has so far required strong reasoning agents such as GPT-4o. Furthermore, its optimized inference strategy consistently yields more than a 1.5x inference speedup over previous approaches. Finally, this work releases GangnamLoop, a novel and practical multi-trip outdoor benchmark collected by deploying a real quadruped robot on public streets with the anonymization policy. It revisits the same locations under different outdoor conditions and pairs the robot's low viewpoint with the human owner's. The source codes and datasets are publicly available at https://github.com/ndb796/BinaryTracking
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