构建实验室导航新平台，让机器人精准操作仪器
BioVLN: A Simulation Platform for Visual Language Navigation in Biomedical Laboratories
- 用三区域模型定义仪器：本体、安全区、操作面
- 几何探索成功率74.4%~87.5%，多位置采样提升至83.3%~92.5%
- 专为生物实验环境设计，适合机器人导航研究者
生物医学实验室机器人需在执行操作前定位仪器。现有具身导航平台针对家庭环境设计，将目标视为物体中心或任意邻近位置，该表示法不适用于实验室仪器——仪器需从操作侧接近，并保持与周边设备的安全距离。本文提出BioVLN，一个用于开发和评估生物医学实验室中视觉-语言导航智能体的仿真平台。BioVLN为每个仪器定义三个区域：物理本体、周围安全区以及可操作侧前方的操作区。该模型统一应用于场景生成、目标放置、导航评估与安全分析，成功依赖于能否从可操作位置接近仪器。BioVLN支持程序化场景生成与手动设计环境，共生成47个场景和1667个任务回合。提供标准化导航与强化学习接口，便于轨迹收集与策略训练。实验表明，几何探索方法成功率达74.4%–87.5%，而对操作区进行多位置采样可将成功率提升至83.3%–92.5%，并减少不安全靠近行为。
原文摘要 · Abstract (English)
Biomedical laboratory robots must navigate to instruments before performing experimental procedures. Existing embodied navigation platforms are designed for household environments and treat a target as an object center or an arbitrary nearby position. This representation is inadequate for laboratory instruments, which must be approached from their operating side while maintaining safe clearance from surrounding equipment. We introduce BioVLN, a simulation platform for developing and evaluating visual-language navigation agents in biomedical laboratories. BioVLN represents each instrument with three regions: its physical body, a surrounding clearance region, and an operation area in front of the usable side. This model is applied consistently to scene generation, target placement, navigation evaluation, and safety analysis, so success depends on reaching a position from which the instrument can be accessed. BioVLN supports procedural scene generation and manually designed environments, producing 47 scenes and 1667 episodes. Standardized navigation and reinforcement-learning interfaces enable trajectory collection and policy training. Experiments show that geometric exploration reaches 74.4--87.5% success, while sampling multiple valid positions in the operation area improves success to 83.3--92.5% and reduces unsafe proximity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。