Simulation and AI help Leeds robot conquer clutter

Object manipulation and grasping are notoriously difficult for robots, especially when the target is surrounded by other objects. For humans, planning a route for your hand through a cluttered table in order to pick up an apple is second nature. For a robot, the computation needed may be so extreme that it takes minutes to figure out its strategy, and even then it will often fail in the task.

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By combining two different approaches to this problem, the Leeds team taught a robotic arm to deal with these situations autonomously and efficiently. Automated planning uses computer vision and software to simulate the sequence of moves that might be used to achieve the goal. These simulations can be useful, but don’t replicate the complexity of performing the operations in the real world. To counter this, the researchers also used machine learning to train the robot in around 10,000 trial and error situations to refine its technique.