RoboBrain 2.5 teaches robots to see depth precisely and to keep track of time-aware progress, so plans turn into safe, accurate actions.
Computers usually click like a woodpecker, but they struggle to drag smoothly like a human hand; this paper fixes that.
GR-Dexter is a full package—new robot hands, a smart AI brain, and lots of carefully mixed data—that lets a two-handed robot follow language instructions to do long, tricky tasks.
Dream-VL and Dream-VLA use a diffusion language model backbone to understand images, talk about them, and plan actions better than many regular (autoregressive) models.
Robots learn best from what they would actually see, which is a first-person (egocentric) view, but most AI models are trained on third-person videos and get confused.
Robots usually learn by copying many demonstrations, which is expensive and makes them brittle when things change.
UniUGP is a single system that learns to understand road scenes, explain its thinking, plan safe paths, and even imagine future video frames.