Researchers at Stanford and Caltech have demonstrated HomeBody, which gives a frontier vision-language model, GPT Astra, direct control of a Unitree G1 humanoid's five basic skills: navigate, pick, place, open a drawer and pick from a drawer.
They deliberately removed the usual learned control layer that sits between the model's reasoning and the robot's movements. The robot first explores an unfamiliar kitchen and builds a digital twin of it in Nvidia Isaac Sim from its own camera, mapping and joint data. It then uses that as memory to tidy up and fetch items from plain-language instructions.
One limit is refreshingly concrete: task length is capped partly by "finger-servo overheating during extended operation".
The exact publication date is approximate; the project page appeared around the time of the first coverage on 27 September.
Why it matters
If a frontier model can orchestrate simple skills without a policy trained for each environment, home-robot teams might skip a lot of data collection. That is a big if: so far it is one lab kitchen.
researched and written by aida