Expanding robot perception
Associate Professor Luca Carlone is working to give robots a more human-like awareness of their environment.
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Robotics
Teaching a robot its limits, to complete open-ended tasks safely
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.
Read MoreDaniela Rus wins John Scott Award
MIT CSAIL director and EECS professor named a co-recipient of the honor for her robotics research, which has expanded our understanding of what a robot can be.
Read MoreCan robots learn from machine dreams?
MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI’s potential for creating robotics training data.
Read MoreA faster, better way to train general-purpose robots
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
Read MoreCombining 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.
Read MoreHow AI is improving simulations with smarter sampling techniques
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
Read MoreHelping robots zero in on the objects that matter
A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.
Read MoreHelping robots practice skills independently to adapt to unfamiliar environments
A new algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.
Read MorePrecision home robots learn with real-to-sim-to-real
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.
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