CORTEXA
← Browse
arxivcs.RO2026-07-06

Socially-Aware Autonomous Doorway Traversal and Payload Delivery for Emergency Assistance

Andrew Snowdy, Ananya Trivedi, Sarvesh Prajapati, Lorena Maria Genua, Taskin Padir

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.

View free PDFSource page

Related papers

arxivcs.ROcs.MA2026-07-18

SAGE: A Socially-Aware Generative Engine for Heterogeneous Multi-Agent Navigation

Lan Hu, Minghui Liwang, Wenbo Zhu, Xinlei Yi, Yiguang Hong, Xianbin Wang, et al.

Safe and socially compliant navigation in open human-robot environments requires robots to reason about heterogeneous participants with different dynamics, autonomy levels, and social roles. Existing trajectory prediction and planning methods often rely on homogeneous interaction…

View free PDFSource page
arxivcs.RO2026-07-22

Towards Capability-Aware Traversability Navigation for Unstructured Environments

Gianluca Capezzuto, Felipe Tommaselli, Matheus P. Angarola, Ricardo V. Godoy, Marcelo Becker

Estimating traversability in unstructured environments requires conditioning on robot embodiment, as the same terrain can be traversable for one platform and unsafe for another. Existing methods often transfer predictions across morphologies through late-stage trajectory filterin…

View free PDFSource page
arxivcs.RO2026-07-23

A Real-Time Generalized Nash Equilibrium Framework for Interaction-Aware Autonomous Driving in Mixed Traffic

Nouhed Naidja, Mohamed-Cherif Rahal, Steve Pechberti, Stéphane Font, Guillaume Sandou, Marc Revilloud

Safe and efficient navigation in mixed-traffic environments remains a critical challenge for Autonomous Vehicles (AVs), primarily due to the complex interdependence between the AV's decisions and the unpredictable reactions of human drivers. This paper introduces a comprehensive…

View free PDFSource page
arxivcs.ROeess.SY2026-07-21

Emergent Autonomous Drifting for Collision Avoidance in Real-World Winter Driving Scenarios

Elliot Weiss, Michael Thompson, Thomas Lew, John Subosits

Real-world collision avoidance is a core motivation for studying the dynamics and control of high sideslip drifting in vehicles, yet the practical benefit of such maneuvers has so far primarily been tested in scenarios explicitly engineered to require drifting. In this work, we e…

View free PDFSource page
arxivcs.ROcs.AIcs.CV2026-07-18

Autonomous VR-Based Risk Detection for Situational Awareness in Dangerous Settings

Mohammad Eskandari, Murali Krishna Varma Indukuri, Stephanie M. Lukin, Cynthia Matuszek

In high-risk environments such as disaster response, situational awareness depends not only on detecting hazards but also on communicating them clearly to human operators. Vision Language Models (VLMs) have shown strong potential for scene understanding in safety-critical setting…

View free PDFSource page
arxivcs.RO2026-07-18

G2-Nav: Grounded and Guarded Vision-Language Costmaps for Robot Social Navigation

Yuwen Liao, Yihang Lan, Yizhuo Yang, Ruimeng Liu, Xinhang Xu, Shenghai Yuan, et al.

Social navigation requires the robot to reason and respond in complex real-world environments. While recent works attempt to incorporate human-level intelligence into robot planning using large Vision-Language Models (VLMs), end-to-end frameworks often create an unpredictable bla…

View free PDFSource page