23 citations · 53 across the 69 of their papers we have counts for
5 papers · 2 filters
MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence
Haoran Wen, Wenfu Wang, Kunsong Shi +15
General-purpose robot control requires models to understand task intent, identify where to interact, capture how the scene evolves, and generate precise actions. Vision-language-ac…
ME-Brain-1.0: Memory, Cognition and Action for Evolving Embodied Intelligence
Wei He, Hengtao Li, Zhongrui Yu +20
Current embodied systems largely rely on pretrained capabilities that remain fixed after deployment, limiting their ability to learn from physical interaction. We introduce MachEmb…
What Makes an Efficient VLA? Navigating Action-Head Design, Scaling, and Latency
Luoyang Sun, Guoyang Xia, Fengfa Li +9
Vision-Language-Action (VLA) models combine a pretrained vision encoder, a language backbone, and an action head, but their relative contribution has not been established under con…
BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving
Bing Zhan, Shuyao Shang, Shuo Lu +6
Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, however, typically emphasize only one side of…
ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving
Huimin Wang, Yue Wang, Bihao Cui +7
We introduce ReflectDrive-2, a masked discrete diffusion planner with separate action expert for autonomous driving that represents plans as discrete trajectory tokens and generate…