LLMs change the game
After hitting a wall with its previous approach of trying to pre-program robots to make specific movements, the robotics field has found a way forward with the same approach that’s made LLMs so successful. Axel Krieger, a mechanical engineering researcher focused on the development of surgical systems, spoke on the panel about new research he and his associates would be sharing this week about how the Transformer—the same one that powers tools like ChatGPT, only with robot action as the output—can be used to overcome limitations surrounding how a surgical robotic system called Da Vinci learns tasks via imitation learning. The team demonstrated its findings through the successful execution of three fundamental surgical tasks: tissue manipulation, needle handling, and knot-tying.
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