Unlike traditional robots programmed for specific tasks, RoboCat's abilities expand as it learns new tasks, thanks to its growing breadth of experience. The robot learns from a dataset of millions of trajectories from both prior tasks and new self-generated data. As its techniques improve, RoboCat transfers its newly acquired skills to other robots, allowing them to build upon those skills.
DeepMind's breakthrough reduces the need for human-supervised training and significantly accelerates robotics research. RoboCat's success rate in tackling previously unlearned tasks increased over time through self-training, with its performance doubling. The robot's ability to learn quickly with just 100 demonstrations is attributed to drawing from a large and diverse dataset. While this development marks a significant advancement towards creating a general-purpose robot, the question of whether robots will eventually replace human intervention remains open. The 1921 play "R.U.R.: Rossum's Universal Robots" contemplated such possibilities a century ago, envisioning a future where synthetic humanoids replaced human labour. However, RoboCats present a friendlier outlook, promising advancements in robotics research while emphasising cooperation between humans and machines. As humorist Will Rogers once quipped, "Letting the cat out of the bag is a whole lot easier than putting it back in." If you want to know more…, refer to the paper titled "RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation" More on miteradio.com.au (press play)
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