2 citations · 2 across the 5 of their papers we have counts for
5 papers
Latent Action as Intention Enables Efficient Future Imagination for World Action Models
Xiang Li, Yupeng Zheng, Songen Gu +11
World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes t…
Collaborative Learning of Local 3D Occupancy Prediction and Versatile Global Occupancy Mapping
Shanshuai Yuan, Julong Wei, Muer Tie +3
Vision-based 3D semantic occupancy prediction is vital for autonomous driving, enabling unified modeling of static infrastructure and dynamic agents. Global occupancy maps serve as…
OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving
Julong Wei, Shanshuai Yuan, Pengfei Li +3
The rise of multi-modal large language models(MLLMs) has spurred their applications in autonomous driving. Recent MLLM-based methods perform action by learning a direct mapping fro…
O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation
Muer Tie, Julong Wei, Zhengjun Wang +7
Online construction of open-ended language scenes is crucial for robotic applications, where open-vocabulary interactive scene understanding is required. Recently, neural implicit…
HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in Urban Scenes
Ke Wu, Kaizhao Zhang, Zhiwei Zhang +7
Online dense mapping of urban scenes forms a fundamental cornerstone for scene understanding and navigation of autonomous vehicles. Recent advancements in mapping methods are mainl…