A Revolution in How Robots Learn
A Revolution in How Robots Learn
The article by James Somers explores the rapid advancements in robotics, driven by artificial intelligence. It details how robots are moving from being explicitly programmed for specific tasks to learning through methods like imitation learning and reinforcement learning, similar to how human children learn. Somers visits Google DeepMind to witness projects like ALOHA, where robots learn complex dexterous tasks such as tying shoelaces by observing human operators, and a Ping-Pong playing robot that adapts and improves its game by playing against humans. The piece discusses the concept of 'foundation models' for robotics, where a single AI could control various types of robots, and touches upon the societal and ethical implications of creating autonomous, intelligent machines that could reshape industries and human labor.
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the article ends with a quick set of ideas that have all the real meat and potatoes.
what does human in the loop look like for one singular model driven robotics? our chatbots are all tied to centralized services, will robots all be tied to the internet as well (enter thoughts of skynet)?
reading about the lego brick that is losing its color really hammers home the challenges of applying current training approaches to robots. we are seeing big gains, but can also continue to appreciate what human children do in a few short years.
the impact trajectory does feel similar to LLMs, in that lower wage jobs are being targeted ...
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