A pair of co-designed, three-finger, nine-DoF dexterous manipulation interface: one wearable for data collection, one for robot deployment.

A generative world model for action-controllable, long-horizon robotic simulation. Built on next-scale autoregression with scale-compressed streaming memory and on-policy alignment, it generates visual futures in a coarse-to-fine manner, achieving calibrated task outcomes and consistent policy rankings across 600+ matched sim-to-real closed-loop rollouts.

A training-free framework that enables robots to acquire new manipulation skills in seconds from a single human video while retaining previously mastered capabilities.

A multimodal action tokenizer that doubles as a semantic interface between vision-language reasoning and continuous robot control.

XRZero-G0 is an embodied data acquisition and strategy learning system designed through deep collaboration between hardware and software. It aims to overcome the fundamental bottleneck of acquiring high-quality, motion-aligned demonstration data in the field of dexterous robot operation.

Carving World Action Modeling at Their Event Joints

fully open-source, trained with gradient-bridged co-training, and deployable on real robots straight from pretraining.

An end-to-end embodied foundation model that leverages large-scale multimodal pretraining to achieve (1) embodiment-aware vision--language understanding, (2) strong language--action association, and (3) robust manipulation capability.