arXiv：cs.AI· Francesco Baldo, Luca Castri, Gloria Beraldo, Nicola Bellotto·· 22 小时前
社会导航信号在走廊场景中的因果影响估计
Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios
AI 导读
研究用因果模型评估走廊人机交互中社会导航信号是否真正提升通行成功率。结果显示，仅以机器人动作做条件的观察性估计会误判该信号有害，而控制静态障碍等混杂因素后的因果估计表明该信号有益，结论发生反转。该工作说明忽略混杂会导致错误结论，因果模型对社会机器人导航中的情境感知信号决策十分重要。
正文
Abstract:Deploying robots in human-shared environments requires deciding not only how to navigate, but also when dedicated social signals are worth emitting. Classic observation-based inference can be misleading in such settings: confounding factors may bias the estimated relationship between a signal and the task outcome, leading to incorrect decisions. Causal inference provides a framework for disentangling these relationships by modelling the mechanisms linking observations, actions, and outcomes, and by estimating interventional effects after accounting for confounding paths. We study a human-robot interaction scenario in a corridor and use a causal model to estimate whether a social navigation signal causally improves transit success. We represent the interaction as a causal DAG involving robot action, pedestrian occupancy, available space, task outcome, and a controlled confounder (static obstacles). We compare the interventional estimate with the observational estimate obtained by conditioning only on the robot action, which does not explicitly model interventions. Our results show that accounting for confounding variables can reverse the conclusions drawn from naive observational analysis: the observational estimate wrongly suggests that the social signal is detrimental, whereas the causal estimate shows that it is beneficial. This discrepancy highlights how failing to account for confounding can lead to incorrect conclusions and demonstrates the importance of causal models for context-aware signalling decisions in social robot navigation.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.16411 [cs.RO] |
| (or arXiv:2610.16411v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16411 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nicola Bellotto [view email]
[v1]
Mon, 12 Oct 2026 08:46:26 UTC (436 KB)
来源：arXiv：cs.AI · arxiv.org