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Generalizable Autonomy in Robot Manipulation

Part 1: In his first noteworthy lecture, Animesh Garg presents his vision of building intelligent robotic assistants that learn with the same amount of efficiency and generality than humans do through learning algorithms, particularly in robot manipulation. Humans learn through instruction or imitation and can adapt to new situations by drawing from experience. The goal is to have robotic systems recognize new objects in new environments autonomously (diversity) and enable them to do things they were not trained to do by using long-term reasoning (complexity).