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Redefining Robotics with Carolina Parada

Google DeepMind: The Podcast

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Balancing Learning and Safety in Robotics

This chapter explores the interplay between generalization and specialization in robotic learning, focusing on how robots can learn tasks like origami through innovative methods such as reinforcement learning and simulation. It emphasizes the challenges associated with transferring this learning to real-world applications, addressing the sim-to-real gap, environmental conditions, and the need for robust safety protocols. Additionally, the chapter introduces the Asimov dataset and the complexities of developing comprehensive safety rules for robots in unpredictable scenarios.

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