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MIT researchers develop new approach for training general purpose robots
The research could lead to a future where robots are not just specialized tools but flexible assistants that can quickly learn new skills and adapt to changing...
The MIT team's new technique combines large amounts of heterogeneous data from various sources into a single system capable of teaching robots a wide array of tasks. At the heart of the HPT architecture is a transformer, a type of neural network that processes inputs from various sensors, including vision and proprioception data, and creates a shared "language" that the AI model can understand and learn from. But in my view, another big problem is that the data come from so many different domains, modalities, and robot hardware," said Lirui Wang, the lead author of the study and an electrical engineering and computer science (EECS) graduate student at MIT.
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