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 (Cumulative Prospect Theory (CPT), Conditional Value at Risk (CVAR), and Expected Risk (ER). We find that CPT captures people’s decisions more accurately than CVaR and ER, corroborating previous theoretical results that CPT is more expressive and inclusive than CVaR and ER. 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. We find a large majority of participants were interested in navigation XAI and want robots that infer how users plan paths in their environment. A large majority also showed interest in understanding robot’s intention (path plans and decisions) through various modalities like speech, touchscreen and gestures. Several participants also expressed interest in learning the rationale behind robot’s decision through high-level explanations. Our work provides crucial XAI design insights for deployment of robots in RCEs.