5 papers
Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models
Jinhao Wu, Shiduo Zhang, Yicheng Liu +9
Most vision-language-action (VLA) models map observations directly to actions without explicit intermediate planning, which limits performance on long-horizon tasks where early mis…
Conditioning Matters: Training Diffusion Policies is Faster Than You Think
Zibin Dong, Yicheng Liu, Yinchuan Li +2
Diffusion policies have emerged as a mainstream paradigm for building vision-language-action (VLA) models. Although they demonstrate strong robot control capabilities, their traini…
Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving
Yunshen Wang, Yicheng Liu, Tianyuan Yuan +4
Accurately predicting 3D occupancy grids from visual inputs is critical for autonomous driving, but current discriminative methods struggle with noisy data, incomplete observations…
PreSight: Enhancing Autonomous Vehicle Perception with City-Scale NeRF Priors
Tianyuan Yuan, Yucheng Mao, Jiawei Yang +3
Autonomous vehicles rely extensively on perception systems to navigate and interpret their surroundings. Despite significant advancements in these systems recently, challenges pers…
GPT-Driver: Learning to Drive with GPT
Jiageng Mao, Yuxi Qian, Junjie Ye +2
We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge i…