首个基于数据驱动的社交机器人导航评估指标，支持真实与仿真轨迹评分。
Towards Data-Driven Metrics for Social Robot Navigation Benchmarking
- 构建可评分的社交导航轨迹数据集，含4427条轨迹（182条真实+4245条仿真）
- 提出首个端到端学习的导航评分模型SN26，测试损失表现优于人工设计指标
- 数据、代码、模型全开源，适合机器人评测与策略优化研究者使用
本文致力于开发一种数据驱动的社交机器人导航评估指标，以支持地面机器人的基准测试与策略优化。我们阐述了方法动机，并提出社交导航轨迹数据的格式化与存储规范。依据该规范，我们构建了首个版本的数据集，包含4427条轨迹（182条真实采集，4245条仿真生成），经人类评分并完成数据质量检查后，共获得4402条有效评分轨迹。文中首次提出完整的学习型社交导航评估指标SN26，提供定性和定量结果，包括测试损失、与人工设计指标的对比以及消融实验。所有数据、代码及模型权重均公开可用。
原文摘要 · Abstract (English)
This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy optimization for ground robots. We provide the motivations for our approach and describe our proposal to format and store rated social navigation trajectory datasets. Following these guidelines, we compiled a first version of the proposed dataset with 4427 trajectories -- 182 real and 4245 simulated -- and presented it to human raters, yielding a total of 4402 rated trajectories after data quality assurance. Notably, we provide the first all-encompassing learned social robot navigation metric (SN26), along qualitative and quantitative results, including the test loss achieved, a comparison against hand-crafted metrics, and an ablation study. All data, software, and model weights are publicly available.
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