We show that off-the-shelf text-based Transformers, with no additional training, can perform
few-shot in-context visual imitation learning, mapping visual observations to action …

Language Models and Vision Language Models have recently demonstrated unprecedented
capabilities in terms of understanding human intentions, reasoning, scene understanding, …

Recent advancements in large multimodal models have led to the emergence of remarkable
generalist capabilities in digital domains, yet their translation to physical agents such as …

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 propose DINOBot, a novel imitation learning framework for robot manipulation, which
leverages the image-level and pixel-level capabilities of features extracted from Vision …

Large Language Models (LLMs) have recently shown promise as high-level planners for
robots when given access to a selection of low-level skills. However, it is often assumed that …

General-purpose robots need a deep understanding of the physical world, advanced reasoning,
and general and dexterous control. This report introduces the latest generation of the …

We present $\mathbf{R}+\mathbf{X}$ , a framework which enables robots to learn skills from
long, unlabelled, first-person videos of humans performing everyday tasks. Given a …

In this work, we introduce a novel method to learn everyday-like multi-stage tasks from a
single human demonstration, without requiring any prior object knowledge. Inspired by the …

Imitation learning with visual observations is notoriously inefficient when addressed with end-to-end
behavioural cloning methods. In this letter, we explore an alternative paradigm …