Google DeepMind, Intrinsic build AI for multi-robot planning

The article discusses a new AI-driven approach to programming and coordinating multiple industrial robots in shared workspaces, developed through a collaboration between Google DeepMind Robotics, Intrinsic, and University College London. Traditional methods for robot motion planning rely heavily on manual programming, teach pendants, and trial-and-error, which are time-consuming and become increasingly complex when managing multiple robots to avoid collisions. The researchers introduced "RoboBallet," an AI model that leverages reinforcement learning and graph neural networks (GNNs) to generate collision-free motion plans efficiently. This model represents robots, tasks, and obstacles as nodes in a graph and learns generalized planning strategies by training on millions of synthetic scenarios, enabling it to produce near-optimal trajectories rapidly without manual intervention. Intrinsic, a company spun out of Alphabet’s X in 2021, aims to simplify industrial robot programming and scaling. Their RoboBallet system requires only CAD files and high-level task descriptions to generate motion plans, eliminating the need for detailed coding or fine