2 papers
cs.RO2026
MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning
Letian Chen, Yiren Lu, Justin Fu +5
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in semantic understanding and common sense reasoning, making them promising candidates for solvi…
cs.CV2025
S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation
Yichen Xie, Runsheng Xu, Tong He +9
The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many en…