Learning While Deploying turns real-world robot deployment into a continual reinforcement learning loop, where a shared generalist VLA policy improves from the experience collected by a robot fleet.
Scalable Online Post-Training for VLA Models
From World Model to General Goal-Conditioned Policy
Unified Embodied VLM Reasoning with Robotic Action
