构建双平台双文化社交机器人导航数据集，助力复杂人群环境下的智能导航研究
Bi3: A Biplatform, Bicultural, Biperson Dataset for Social Robot Navigation
- 设计真实交互场景，记录双人与机器人近距离互动轨迹
- 覆盖10.5小时多模态数据，包含运动轨迹、视频与用户评价
- 适合研究人机共融导航、行为预测及跨文化适应的团队使用
我们提出Bi3，一个在受限实验室空间中收集的社交机器人与人群交互导航数据集。相较于以往工作，该数据集独特之处在于：设计了两人与机器人近距离互动的实验场景；涵盖五种导航算法；使用两种不同机器人平台；招募来自美国和法国的74名参与者；采集10.5小时的人类与机器人真值运动轨迹、RGB视频及对机器人表现的主观评价。通过交互密度与人类速度等指标分析表明，Bi3具备独特的多样性与建模复杂性。该数据集有助于理解人与机器人如何在受限空间中协同活动，可作为训练人类运动预测模型与密集人群导航控制策略的重要资源。
原文摘要 · Abstract (English)
We contribute Bi3, a dataset of social robot navigation among groups of people in a constrained lab space. Compared to prior data collection efforts for social robot navigation, our dataset is unique in that it features: an original experiment design giving rise to close navigation encounters between two humans and a robot; five different navigation algorithms; two different robot platforms; a diverse participant pool of 74 people recruited from two sites in the USA and France; multimodal data streams including 10.5 hours of human and robot ground-truth motion tracks, RGB video, and user impressions over robot performance. Our analysis of the collected dataset through metrics like interaction density and human velocity suggests that Bi3 represents a benchmark of unique diversity and modeling complexity. Bi3 contributes towards understanding how humans and robots can productively mesh their activities in constrained environments, and can be a resource for training models of human motion prediction and robot control policies for navigation in densely crowded spaces.
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