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Bookmark or Share- Can robots learn from machine dreams? - MIT NewsSince the 1970s, the field has evolved from writing sophisticated programs to using deep learning, teaching robots to learn directly from human behavior. But a critical bottleneck remains: data quality. To improve, robots need to encounter scenarios that push the boundaries of their capabilities, operating at the edge of their mastery.
- Robotics | MIT News | Massachusetts Institute of TechnologyCombining next-token prediction and video diffusion in computer vision and robotics. A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments. October 16, 2024. Read full story →
- A technique for more effective multipurpose robots - MIT NewsBut robots trained this way, with a relatively small amount of task-specific data, are often unable to perform new tasks in unfamiliar environments. In an effort to train better multipurpose robots, MIT researchers developed a technique to combine multiple sources of data across domains, modalities, and tasks using a type of generative AI known ...
- Flocks of assembler robots show potential for making larger structuresMIT researchers made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots. Their new study shows both assembler bots and components of the structure being built can all be made of the same subunits, and robots can move independently in large numbers to ...
- Dexterous robotic hands manipulate thousands of objects with easeThis type of “in-hand” object reorientation has been a challenging problem in robotics, due to the large number of motors to be controlled and the frequent change in contact state between the fingers and the objects. And with over 2,000 objects, the model had a lot to learn. The problem becomes even more tricky when the hand is facing ...
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