AI-assisted learning platform helps robots reason beyond literal commands
The new approach, known as FLEX, could lead to more intuitive, interactive household and assistive robots.
The new approach, known as FLEX, could lead to more intuitive, interactive household and assistive robots.
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Read MoreAssociate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Read MoreThe “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
Read MoreAtlas Building Composites is commercializing MIT research to turn plastic waste into parts for buildings and other infrastructure.
Read MoreA new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
Read MoreDirector of CSAIL and MIT professor honored for her contributions to robotics, artificial intelligence, and autonomous systems.
Read MoreMIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.
Read More“SceneSmith” system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores.
Read MoreMIT researchers developed FloatForm, a swarm of small aquatic robots that snap together like ants forming a raft, assembling into reconfigurable structures on the water.
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