斯坦福 HomeBody 用 Astra 驱动机器人做家务
Unitree G1 先探索厨房建空间记忆，再由 GPT-6 Astra 规划导航、抓取、开抽屉等多步任务，无需专门训练数据。
GPT-6 Astra+Unitree G1->home-humanoid Stanford’s HomeBody is testing a different way to build humanoid intelligence. A Unitree G1 first explores an unfamiliar kitchen, builds persistent spatial memory and a Real2Sim digital twin, then uses GPT-6 Astra to plan and compose skills like Navigate, Pick, Place and Open Drawer. It can handle multi-step tasks across the room without environment-specific training data or additional policy learning. There’s no learned VLA sitting between the VLM and the robot’s skills. The VLM decides what to do. Skills handle how to do it. Execution results are fed back to the model, allowing it to revise the next step when something fails. It inevitably makes one reflect--> As general models become better at spatial reasoning, planning and generalization, how much more do we actually need to build for the robot stack?