Beyond Collision Avoidance: Multi-Robot Yielding and Spatial Affordance in Emergency Evacuations

Agent-based evacuation simulations are widely used to study crowd behavior during emergencies, but many models rely on assumptions such as perfect event awareness, complete exit knowledge, and fully rational decision-making. This paper presents an extended evacuation framework that integrates cognitive, emotional, social, and personality-related mechanisms into a unified model of human behavior under uncertainty. The framework incorporates a dynamic event-awareness mechanism based on a continuous Event Certainty Level, a memory-based representation of exit knowledge subject to acquisition, forgetting, and recall, a continuous fear model in which panic emerges as a high-intensity state, and an OCEAN-based personality representation. Neuroticism is explicitly integrated into the emotional model, influencing fear generation, escalation, social contagion, and recovery. Behavioral heterogeneity is further captured through individualized decision thresholds that affect responses to perceived risk. The framework is evaluated through simulation experiments examining the effects of spatial familiarity, memory robustness, decision sensitivity, emotional dynamics, and personality variation. Results show that cognitive, emotional, and personality-driven processes substantially influence evacuation dynamics, reducing evacuation efficiency and generating realistic crowd phenomena such as delays, confusion, injuries, and socially influenced behaviors. The proposed framework provides a more realistic representation of human behavior in emergency evacuations and supports systematic investigation of the interactions between cognition, emotion, personality, and crowd dynamics.

Robot initiative is a central challenge in multi-party human-robot collaboration. A robot that contributes without being addressed may provide timely support, but it may also disrupt coordination, divide attention, or interrupt turn-taking; a robot that waits to be addressed may preserve human control, but it may also miss opportunities to assist. We investigate this design challenge in a collaborative escape room in which pairs of participants work with a humanoid robot under either a reactive interaction model, where the robot responds only when addressed, or a proactive model, where it listens continuously, contributes autonomously, and periodically re-initiates interaction. We evaluate both models using puzzle-solving performance, interaction frequency, and participant ratings on the Godspeed and RoSAS scales. The proactive model substantially increases interaction frequency, whereas the reactive model shows a descriptively higher overall success rate (92.86% vs. 71.42%). The strongest differences emerge when prior experience and personality are taken into account: participants with LLM experience solve the early puzzles faster in the reactive condition, and participants with prior robot experience show modified evaluations of proactive and reactive interaction as do introverted participants. These findings demonstrate that the effects of robot initiative are simultaneously shaped by users' prior experience, personality traits and more generally by the needs of the group.

In this work, we focus on the scenario of a robot-assisted emergency evacuation. We consider two capabilities relevant to such a setting. The first is opening doors ahead of the people being evacuated, so that their path toward an exit stays clear. The second is retrieving rescue equipment and delivering it to the emergency responders carrying out the evacuation. From a systems perspective, this involves several tasks at once. The robot must locate ADA-compliant door buttons and the rescue equipment it needs to retrieve. Additionally, it must remain aware of the people around it and adapt its behavior to them, so that it supports the evacuation rather than getting in the way. We address these demands with a behavior tree at the core of our framework. This structure is chosen for its ability to select high-level tasks based on environmental triggers, and to extend to new situations as they arise. We evaluate the system in 105 trials on the Toyota Human Support Robot, across five hardware and three simulation scenarios. These trials capture the decisions the robot must make in this setting: whether to press a door button, yield to a nearby person, walk through a door someone else is holding, or first retrieve rescue equipment before traversing the door. Overall, the system completes 97 of the 105 trials successfully. These results suggest our framework provides a practical basis for robotic assistance in broader emergency response tasks. Code and video demonstrations are available at https://github.com/AndrewSnowdy/hsr_mm_control.