These robots were tested in 30 unfamiliar Bay Area homes with no extra training or data from those homes. Tidying rooms, fold towels and make beds using what it already learned. This kind of real-world generalization is a big step for humanoid robots. The conversation heavily emphasized zero-shot generalization, resolving the longstanding brittle-policy problem in robotic manipulation tasks. By leveraging massive pre-trained multi-modal embeddings, the robot's policy network maps visual inputs directly to joint torques without task-specific retraining. This emergent zero-shot capability allows the platform to successfully execute novel manipulation workflows and handle unstructured environments dynamically. The CEO (Brett Adcock) detailed the architectural shift from traditional hierarchical pipeline control to unified neural network architectures for perception and control. The convergence of edge compute optimization, sensor-fusion precision and end-to-end learning is compressing the timeline for autonomous deployment. still Hope @continuumlabs_ launching soon waiting all eagerly