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- Beijing Academy of Artificial IntelligenceCN102 papers
- Shanghai Jiao Tong UniversityCN46 papers
- Chinese University of Hong KongHK26 papers
- Tsinghua UniversityCN25 papers
- Fudan UniversityCN21 papers
- Group Sense (China)CN17 papers
- Northwestern Polytechnical UniversityCN14 papers
- ShangHai JiAi Genetics & IVF InstituteCN14 papers
- University of Science and Technology of ChinaCN14 papers
- University of Hong KongHK13 papers
- Peking UniversityCN11 papers
- Tongji UniversityCN11 papers
5 papers · 1 filter
WAND: Learning Robust Navigation under Complex Wind Disturbances and Dense Obstacles for Quadrotors
Zhonghan Tang, Chenhui Li, Shuai Liang +5
Robust navigation in cluttered environments remains a fundamental challenge for quadrotors, particularly when strong wind disturbances arise, which perturb vehicle dynamics, limit…
UniCon: A Unified System for Efficient Robot Learning Transfers
Yunfeng Lin, Li Xu, Yong Yu +2
Deploying learning-based controllers across heterogeneous robots is challenging due to platform differences, inconsistent interfaces, and inefficient middleware. To address these i…
LLM-GROP: Visually Grounded Robot Task and Motion Planning with Large Language Models
Xiaohan Zhang, Yan Ding, Yohei Hayamizu +6
Task planning and motion planning are two of the most important problems in robotics, where task planning methods help robots achieve high-level goals and motion planning methods m…
Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy
Liansheng Wang, Xinke Zhang, Chenhui Li +3
LiDAR-Inertial Odometry (LIO) is a foundational technique for autonomous systems, yet its deployment on resource-constrained platforms remains challenging due to computational and…
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Xinyu Cai, Wentao Jiang, Runsheng Xu +4
Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field; however, how to…