# Robot Navigation in Risky, Crowded Environments: Understanding Human Preferences

arXiv: https://arxiv.org/abs/2303.08284 · HTML: https://mlsanity.com/paper/2303.08284/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics](https://mlsanity.com/paper/1710.11040/) (2017)
- [Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior](https://mlsanity.com/paper/1805.08010/) (2018)
- [When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans](https://mlsanity.com/paper/2001.04377/) (2020)
- [Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles](https://mlsanity.com/paper/2003.11675/) (2020)
- [Exploiting Natural Language for Efficient Risk-Aware Multi-robot SaR Planning](https://mlsanity.com/paper/2104.03809/) (2021)
- [CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model](https://mlsanity.com/paper/2105.15189/) (2021)
- [Risk-Aware Decision Making in Service Robots to Minimize Risk of Patient Falls in Hospitals](https://mlsanity.com/paper/2010.08124/) (2020)
- [Gesture based Human-Swarm Interactions for Formation Control using interpreters](https://mlsanity.com/paper/1804.08676/) (2018)
- [Planning under non-rational perception of uncertain spatial costs](https://mlsanity.com/paper/1904.02851/) (2019)
- [Risk-perception-aware control design under dynamic spatial risks](https://mlsanity.com/paper/2109.04570/) (2021)

## Built on by (most cited first, 2 of 2)

- [Predicting Human Perceptions of Robot Performance During Navigation Tasks](https://mlsanity.com/paper/2310.11590/) (2023)
- [The Social Context of Human-Robot Interactions](https://mlsanity.com/paper/2508.13982/) (2025)

## Abstract (arXiv)

Risky and crowded environments (RCE) contain abstract sources of risk and uncertainty, which are perceived differently by humans, leading to a variety of behaviors. Thus, robots deployed in RCEs, need to exhibit diverse perception and planning capabilities in order to interpret other human agents' behavior and act accordingly in such environments. To understand this problem domain, we conducted a study to explore human path choices in RCEs, enabling better robotic navigational explainable AI (XAI) designs. We created a novel COVID-19 pandemic grocery shopping scenario which had time-risk tradeoffs, and acquired users' path preferences. We found that participants showcase a variety of path preferences: from risky and urgent to safe and relaxed. To model users' decision making, we evaluated three popular risk models (Cumulative Prospect Theory (CPT), Conditional Value at Risk (CVAR), and Expected Risk (ER). We found that CPT captured people's decision making more accurately than CVaR and ER, corroborating theoretical results that CPT is more expressive and inclusive than CVaR and ER. We also found that people's self assessments of risk and time-urgency do not correlate with their path preferences in RCEs. Finally, we conducted thematic analysis of open-ended questions, providing crucial design insights for robots is RCE. Thus, through this study, we provide novel and critical insights about human behavior and perception to help design better navigational explainable AI (XAI) in RCEs.