Interlocking AIs let robots pick and place faster than ever
By Editor - Wed Nov 18, 12:22 pm
One of the jobs for which robots are best suited is the tedious, repetitive “pick and place” task common in warehouses — but humans are still much better at it. UC Berkeley researchers are picking up the pace with a pair of machine learning models that work together to let a robot arm plan its grasp and path in just milliseconds. People don’t have to think hard about how to pick up an object and put it down somewhere else — it’s not only something we’ve had years of practice doing every day, but our senses and brains are well adapted for the task. No one thinks, “what if I picked up the cup, then jerked it really far up and then sideways, then really slowly down onto the table” — the paths we might move an object along are limited and usually pretty efficient. Robots, however, don’t have common sense or intuition. Lacking an “obvious” solution, they need to evaluate thousands of potential paths for picking up an object and moving it, and that involves calculating the forces involved, potential collisions, whether it affects the type of grip that should be used, and so on. Once the robot decides what to do it can execute quickly, but that decision takes time — several seconds at best, and possibly much more depending on the situation.
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