Transformer architectures have facilitated the development of large-scale and general-
purpose sequence models for prediction tasks in natural language processing and computer …

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine
learning models can solve specific downstream tasks either zero-shot or with small task …

Transformers have revolutionized vision and natural language processing with their ability to
scale with large datasets. But in robotic manipulation, data is both limited and expensive …

Background Medicine is inherently multimodal, requiring the simultaneous interpretation
and integration of insights between many data modalities spanning text, imaging, genomics …

A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks.
Recent progress in text-guided image synthesis has yielded models with an impressive …

In this work, we present a scalable reinforcement learning method for training multi-task
policies from large offline datasets that can leverage both human demonstrations and …

Large transformer models trained on diverse datasets have shown a remarkable ability to
learn in-context, achieving high few-shot performance on tasks they were not explicitly …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

Constructing AI models that respond to text instructions is challenging, especially for
sequential decision-making tasks. This work introduces an instruction-tuned Video …

Transformers, originally devised for natural language processing (NLP), have also produced
significant successes in computer vision (CV). Due to their strong expression power …