Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …

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 language models (LLMs) are shown to possess a wealth of actionable knowledge that
can be extracted for robot manipulation in the form of reasoning and planning. Despite the …

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 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 …

Embodied AI is a crucial frontier in robotics, capable of planning and executing action
sequences for robots to accomplish long-horizon tasks in physical environments. In this …

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 …

We present the largest and most comprehensive empirical study of pre-trained visual
representations (PVRs) or visual 'foundation models' for Embodied AI. First, we curate …