Precision 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.
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.
MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
This technique could lead to safer autonomous vehicles, more efficient AR/VR headsets, or faster warehouse robots.
LLMs trained primarily on text can generate complex visual concepts through code with self-correction. Researchers used these illustrations to train an image-free computer vision system to recognize real photos.
The method uses language-based inputs instead of costly visual data to direct a robot through a multistep navigation task.
DenseAV, developed at MIT, learns to parse and understand the meaning of language just by watching videos of people talking, with potential applications in multimedia search, language learning, and robotics.
The technique characterizes a material’s electronic properties 85 times faster than conventional methods.
A new approach could streamline virtual training processes or aid clinicians in reviewing diagnostic videos.
“Alchemist” system adjusts the material attributes of specific objects within images to potentially modify video game models to fit different environments, fine-tune VFX, and diversify robotic training.
Fifteen new faculty members join six of the school’s academic departments.
Associate Professor Jonathan Ragan-Kelley optimizes how computer graphics and images are processed for the hardware of today and tomorrow.
Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.
MIT Sea Grant students apply machine learning to support local aquaculture hatcheries.
Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.
FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.