Researchers from Stanford and Caltech have built HomeBody, a system that lets a Unitree G1 robot autonomously navigate an unfamiliar kitchen, tidy up, and fetch items from drawers. It’s yet another test showing GPT-6 Astra works well with robots.

HomeBody drops the typical trained control layer between language model and robot. Instead, a swappable vision-language model (VLM), here GPT Astra, calls directly into an extensible skill library for grasping, navigating, or opening drawers. The robot explores the room first, builds a digital twin in Nvidia’s Isaac Sim, and logs objects and locations in spatial memory. That lets it find items even after they leave its field of view. For tasks like “clean up the kitchen,” the language model plans each step and self-corrects on errors.

Limitations include Astra’s latency, overheating finger servos, and high compute costs. The code is on GitHub. Earlier benchmarks showed Astra’s greatly improved spatial reasoning, while another flagged safety issues when Astra controls a robot. OpenAI has already announced plans to get back into robotics, including for personal use.