activity
20212024
most citedA Protocol for Validating Social Navigation Policies

6 citations · 16 across the 7 of their papers we have counts for

collaborators

6 papers

cs.RO20222 cited

Learning Real-world Autonomous Navigation by Self-Supervised Environment Synthesis

Zifan Xu, Anirudh Nair, Xuesu Xiao +1

Machine learning approaches have recently enabled autonomous navigation for mobile robots in a data-driven manner. Since most existing learning-based navigation systems are trained…

cs.RO2022

Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways

Jin-Soo Park, Xuesu Xiao, Garrett Warnell +2

While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple r…

cs.RO20223 cited

Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation

Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski +14

Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e.g., in cluttered home environments or in human-occupied publi…

cs.RO20226 cited

A Protocol for Validating Social Navigation Policies

Sören Pirk, Edward Lee, Xuesu Xiao +3

Enabling socially acceptable behavior for situated agents is a major goal of recent robotics research. Robots should not only operate safely around humans, but also abide by comple…

cs.RO2021

Visual Representation Learning for Preference-Aware Path Planning

Kavan Singh Sikand, Sadegh Rabiee, Adam Uccello +3

Autonomous mobile robots deployed in outdoor environments must reason about different types of terrain for both safety (e.g., prefer dirt over mud) and deployer preferences (e.g.,…

cs.RO20212 cited

Incorporating Gaze into Social Navigation

Justin Hart, Reuth Mirsky, Xuesu Xiao +1

Most current approaches to social navigation focus on the trajectory and position of participants in the interaction. Our current work on the topic focuses on integrating gaze into…