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RoboBallet The Dance Of Cooperative Robots
The RoboBallet system helps teams of automated robots work together:
(Four-handed RoboBallet)
Scientists at UCL, Google DeepMind and Intrinsic have developed a powerful new AI algorithm that enables large sets of robotic arms to work together faster and smarter in busy industrial settings...Co-author Associate Professor Alex Li from UCL Computer Science said, "In today's factories, coordinating multiple robotic arms is like solving a moving 3D puzzle, every action must be perfectly timed and placed to avoid collisions. Right now, this planning takes specialists hundreds of hours and is costly to design manually.
"The name, RoboBallet, captures the elegance and what we can do with so many robots. Just as ballet dancers move in perfect harmony with each other, our robots can now coordinate their movements with a superhuman level of precision and grace..."
(Via TechXplore.)Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may be simple in isolation, automated joint task allocation, scheduling, and motion planning under spatiotemporal constraints remain computationally intractable for classical methods at real-world scales.
Existing multiarm systems deployed in industry rely on human intuition and experience to design feasible trajectories manually in a labor-intensive process. To address this challenge, we propose a reinforcement learning (RL) framework to achieve automated task and motion planning, tested in an obstacle-rich environment with eight robots performing 40 reaching tasks in a shared workspace, where any robot can perform any task in any order.
Our approach builds on a graph neural network (GNN) policy trained via RL on procedurally generated environments with diverse obstacle layouts, robot configurations, and task distributions. It uses a graph representation of scenes and a graph policy neural network trained through RL to generate trajectories of multiple robots, jointly solving the subproblems of task allocation, scheduling, and motion planning.
Trained on large randomly generated task sets in simulation, our policy generalizes zero-shot to unseen settings with varying robot placements, obstacle geometries, and task poses.
(Via RoboBallet: Planning for multirobot reaching with graph neural networks and reinforcement learning.)
Robots working together has probably been thought about since R.U.R. (Rossum's Universal Robots), the play by Karel Capek, debuted in Prague in 1921 and gave us the word robot.
But when I think of robots working together in tight coordination, I think of the robot team that Isaac Asimov describes in Catch That Rabbit (1944):
Powell regarded Dave — laymen might think of robots by their serial numbers : roboticists never — with approval. It was not over-massive by any means, in spite of its construction as thinking-unit of an integrated seven-unit robot team. It was seven feet tall, and a lousy half-ton of metal and electricity. A lot? Not when that half-ton has to be a mass of condensers, circuits, relays, and vacuum cells that can handle practically any psychological reaction known to humans. And a positronic brain, which with ten pounds of matter and a few quintillion quintillions of positrons runs the whole show.
(Subsidiary robots in 'Catch that Rabbit' by Isaac Asimov)"Look, that robot, DV-5, has six robots under it. And not just under it - they're part of it..."
He watched the posturings of the robots on the visiplate. They were bronzy gleams of smooth motion against the shadowy crags of the airless asteroid. There was a marching formation now, and in their own dim body light, the rough-hewn walls of the mine tunnel swam past noiselessly, checkered with misty erratic blobs of shadow. They marched in unison, seven of them, with Dave at the head. They wheeled and turned in macabre simultaneity; and melted through changes of formation with the weird ease of chorus dancers in Lunar Bowl.
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