Helping robots practice skills independently to adapt to unfamiliar environments
New algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.
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New algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.
CSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.
Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.
The method uses language-based inputs instead of costly visual data to direct a robot through a multistep navigation task.
With generative AI models, researchers combined robotics data from different sources to help robots learn better.
Fifteen new faculty members join six of the school’s academic departments.
The 10 Design Fellows are MIT graduate students working at the intersection of design and multiple disciplines across the Institute.
A new “consensus game,” developed by MIT CSAIL researchers, elevates AI’s text comprehension and generation skills.
A new algorithm learns to squish, bend, or stretch a robot’s entire body to accomplish diverse tasks like avoiding obstacles or retrieving items.
Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.