How Boston Dynamics’ humanoid robot brain adapts to factory moves

Boston Dynamics has revealed detailed insights into the software powering its next-generation humanoid robot, Atlas, focusing on its application in flexible, real-world factory environments. Unlike traditional automation relying on hand-coded movements, Atlas uses learning-based methods—such as demonstrations, feedback, and refinement—to adapt to the unpredictability of modern manufacturing floors. This approach aims to overcome the economic and time constraints of conventional automation, which often requires costly, task-specific machines that take years to develop. Instead, Boston Dynamics envisions a reprogrammable humanoid robot that can be rapidly redeployed across diverse tasks, particularly in complex settings like automotive plants producing multiple vehicle models with numerous part variations. To build Atlas’s intelligence, Boston Dynamics employs a hybrid strategy combining three parallel methods: teleoperation via virtual reality for precise training data, reinforcement learning in simulation to practice millions of movements, and longer-term observation-based learning where robots gain physical intuition by watching humans. The company rejects a single end-to-end AI model, opting instead for a layered