most citedIf LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents

14 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2025

EAROL: Environmental Augmented Perception-Aware Planning and Robust Odometry via Downward-Mounted Tilted LiDAR

Xinkai Liang, Yigu Ge, Yangxi Shi +3

To address the challenges of localization drift and perception-planning coupling in unmanned aerial vehicles (UAVs) operating in open-top scenarios (e.g., collapsed buildings, roof…

cs.CV2025

Sage Deer: A Super-Aligned Driving Generalist Is Your Copilot

Hao Lu, Jiaqi Tang, Jiyao Wang +14

The intelligent driving cockpit, an important part of intelligent driving, needs to match different users' comfort, interaction, and safety needs. This paper aims to build a Super-…

cs.RO2025

STAMICS: Splat, Track And Map with Integrated Consistency and Semantics for Dense RGB-D SLAM

Yongxu Wang, Xu Cao, Weiyun Yi +1

Simultaneous Localization and Mapping (SLAM) is a critical task in robotics, enabling systems to autonomously navigate and understand complex environments. Current SLAM approaches…

cs.AI20241 cited

On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation

Can Cui, Zichong Yang, Yupeng Zhou +11

Personalized driving refers to an autonomous vehicle's ability to adapt its driving behavior or control strategies to match individual users' preferences and driving styles while m…

cs.CL202414 cited

If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents

Ke Yang, Jiateng Liu, John Wu +9

The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural langua…