Russell Tedrake

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 PhysicsGen system, developed by MIT researchers, helps robots handle items in homes and factories by tailoring training data to a particular machine.

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.

Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.

With generative AI models, researchers combined robotics data from different sources to help robots learn better.