reinforcement learning

Caption:The “steerable scene generation” system creates digital scenes of things like kitchens, living rooms, and restaurants that engineers can use to simulate lots of real-world robot interactions and scenarios.

New tool from MIT CSAIL creates realistic virtual kitchens and living rooms where simulated robots can interact with models of real-world objects, scaling up training data for robot foundation models.

The career development chair recipients are Cheng-Zhi Anna Huang, Kuikui Liu, Marzyeh Ghassemi, Kaiming He, and Alexander Rives.

Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.

Using generative AI to diversify virtual training grounds for robots

The Department of EECS announces new Career Development chairs

A more effective way to train machines for uncertain, real-world situations