Kaixian Qu

SE(2) Navigation Mesh

Shuyang Shi, Kaixian Qu, Changan Chen, Ines Kast et autres

Global navigation for ground robots in complex multi-level environments requires representations that accurately capture traversable regions while enabling efficient path planning. Current approaches present key limitations: Point clouds and volumetric occupancy maps lack explicit surface structure for traversability estimation, whereas direct pathfinding …

An efficient beam search algorithm for active perception in mobile robotics

Kaixian Qu, Han Wang, Victor Klemm, César Cadena et autres

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 observations for accomplishing its mission. Existing approaches either solve a computationally expensive traveling …

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An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics

Kaixian Qu, Han Wang, Victor Klemm, Cesar Cadena et autres

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 observations for accomplishing its mission. Existing approaches either solve a computationally expensive traveling …

Using large language models for embodied planning introduces systematic safety risks

Tao Zhang, Kaixian Qu, Zhibin Li, Jiajun Wu et autres

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 introduce DESPITE, a benchmark of 12,279 tasks spanning physical and normative dangers with fully deterministic validation. …

DESPITE

Tao Zhang, Fan Shi, Zhibin Li, Jiajun Wu et autres

DESPITE

Tao Zhang, Kaixian Qu, Zhibin Li, Jiajun Wu et autres

FunFact: Building Probabilistic Functional 3D Scene Graphs via Factor-Graph Reasoning

Zhengyu Fu, René Zurbrügg, Kaixian Qu, Marc Pollefeys et autres

Recent work in 3D scene understanding is moving beyond purely spatial analysis toward functional scene understanding. However, existing methods often consider functional relationships between object pairs in isolation, failing to capture the scene-wide interdependence that humans use to resolve ambiguity. We introduce …

A robot operating system framework for using large language models in embodied AI

Christopher E. Mower, Yuhui Wan, Hongzhan Yu, Antoine Grosnit et autres

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A Pragmatist Robot: Learning to Plan Tasks by Experiencing the Real World

Kaixian Qu, Guowei Lan, René Zurbrügg, Changan Chen et autres

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 are not inherently aligned with the embodiment, skill sets, and limitations of real-world robotic systems. Inspired by …

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pragmabot

Kaixian Qu

Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Acceleration

Yuntao Ma, Yang Liu, Kaixian Qu, Marco Hutter

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 and model-based control for prehensile whole-body throwing with legged mobile manipulators. Our framework …

ch (code pays fourni par la source)

A Pragmatist Robot: Learning to Plan Tasks by Experiencing the Real World

Kaixian Qu, René Zurbrügg, Changan Chen, Christopher E. Mower et autres

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 are not inherently aligned with the embodiment, skill sets, and limitations of real-world robotic systems. Inspired by …