They go on to explain that the new model is based on DeepMind’s multimodal model Gato . This model can process language, images, and actions in both simulated and physical environments.
The researchers made use of Gato’s architecture that comes with a large training dataset of sequences of images and actions of various robot arms solving hundreds of different tasks.1. Collect 100-1000 demonstrations of a new task or robot, using a robotic arm controlled by a human.3. The spin-off agent practices on this new task/arm an average of 10,000 times, generating more training data.
4. Incorporate the demonstration data and self-generated data into RoboCat’s existing training dataset.This diverse training taught the AI model to operate different robotic arms within a few hours. And RobotCat was quick to adapt. Even though it had not been trained on arms with two-pronged grippers, it was able to adapt to a more complex arm with a three-fingered gripper and twice as many controllable inputs.
The more new tasks it learned, the better it got at learning additional new tasks. Early versions of RoboCat were successful just 36 percent of the time on previously unseen tasks. However, the latest and most advanced RoboCat, which had trained on a greater diversity of tasks, more than doubled this success rate on the same tasks.
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