6 papers
SE(2) Navigation Mesh
Shuyang Shi, Kaixian Qu, Changan Chen +3
Global navigation for ground robots in complex multi-level environments requires representations that accurately capture traversable regions while enabling efficient path planning.…
Using large language models for embodied planning introduces systematic safety risks
Tao Zhang, Kaixian Qu, Zhibin Li +4
Large language models are increasingly used as planners for robotic systems, yet how safely they plan remains an open question. To evaluate safe planning systematically, we introdu…
An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics
Kaixian Qu, Han Wang, Victor Klemm +2
Active perception is a fundamental problem in autonomous robotics in which the robot must decide where to move and what to sense in order to obtain the most informative observation…
FunFact: Building Probabilistic Functional 3D Scene Graphs via Factor-Graph Reasoning
Zhengyu Fu, René Zurbrügg, Kaixian Qu +4
Recent work in 3D scene understanding is moving beyond purely spatial analysis toward functional scene understanding. However, existing methods often consider functional relationsh…
A Pragmatist Robot: Learning to Plan Tasks by Experiencing the Real World
Kaixian Qu, Guowei Lan, René Zurbrügg +4
Large language models (LLMs) have emerged as the dominant paradigm for robotic task planning using natural language instructions. However, trained on general internet data, LLMs ar…
Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Acceleration
Yuntao Ma, Yang Liu, Kaixian Qu +1
Throwing is a fundamental skill that enables robots to manipulate objects in ways that extend beyond the reach of their arms. We present a control framework that combines learning…