A better method for planning complex visual tasks
A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.
computer vision
Improving AI models’ ability to explain their predictions
A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
Read MoreAntonio Torralba, three MIT alumni named 2025 ACM fellows
Torralba’s research focuses on computer vision, machine learning, and human visual perception.
Read MoreA “scientific sandbox” lets researchers explore the evolution of vision systems
The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.
Read MoreDeep-learning model predicts how fruit flies form, cell by cell
The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.
Read MoreTeaching robots to map large environments
A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.
Read More3 Questions: How AI is helping us monitor and support vulnerable ecosystems
MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.
Read MoreMIT tool visualizes and edits “physically impossible” objects
By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.
Read MoreNeural Jacobian Fields, developed by MIT CSAIL researchers, can learn to control any robot from a single camera, without any other sensors.
Read MorePedestrians now walk faster and linger less, researchers find
A computer vision study compares changes in pedestrian behavior since 1980, providing information for urban designers about creating public spaces.
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