Abstract Machine learning (ML) has revolutionized robotics by enhancing perception,
adaptability, decision-making and more, enabling robots to work in complex scenarios …

We study how vision-language models trained on Internet-scale data can be incorporated
directly into end-to-end robotic control to boost generalization and enable emergent …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …

Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

We present WALT, a transformer-based approach for photorealistic video generation via
diffusion modeling. Our approach has two key design decisions. First, we use a causal …

Large policies pretrained on diverse robot datasets have the potential to transform robotic
learning: instead of training new policies from scratch, such generalist robot policies may be …

The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …