Robot Navigation in Risky, Crowded Environments: Understanding Human Preferences

The effective deployment of robots in risky and crowded environments (RCE) requires the specification of robot plans that are consistent with humans' behaviors. As is well known, humans perceive uncertainty and risk in a biased way, which can lead to a diversity of actions and expectations when interacting with others. To gain a better understanding of these behaviors, this work presents new data that aims to verify how these biases translate into a human navigational setting. More precisely, we conduct a novel study that recreates a COVID-19 pandemic grocery shopping scenario and asks participants to select among various paths with different levels of} time-risk tradeoffs. The data shows that participants exhibit a variety of path preferences: from risky and urgent to safe and relaxed. To model users' decision making, we evaluate three popular risk models and found that CPT captures people's decisions more accurately, corroborating previous theoretical results that CPT is more expressive and inclusive. We also find that people's self assessments of risk and time-urgency do not correlate with their path preferences in RCEs. Finally, we conduct thematic analysis of custom open-ended questions to gauge interest and preferences of navigational Explainable AI (XAI) in robots. A large majority also showed interest in understanding robot's intention (path plans and decisions) through various modalities like speech, touchscreen and gestures. Our work provides crucial XAI design insights for deployment of robots in RCEs.

Automated summary

This research aims to understand human preferences and behaviors in risky and crowded environments, specifically in navigational settings. The study shows that individuals have diverse path preferences ranging from risky and urgent to safe and relaxed. It also reveals that self-assessed risk and time-urgency do not correlate with path preferences. Additionally, participants express a high interest in understanding robot intentions and decision-making through various modalities like speech, touchscreen, and gestures. These findings provide crucial insights for the design of explainable AI in robots deployed in risky and crowded environments.