RoboCurate is a way to make better robot training videos by checking if the actions in a generated video actually match what a robot would do in a simulator.
RynnBrain is an open-source 'robot brain' that helps machines see, think, and plan in the real world across space and time.
Robots often get confused by wordy instructions, so this paper tells them exactly where to touch instead of what to do in sentences.
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.