Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

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

Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

We study how visual representations pre-trained on diverse human video data can enable
data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a …

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

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

We present a framework for learning multimodal representations from unlabeled data using
convolution-free Transformer architectures. Specifically, our Video-Audio-Text Transformer …

Imitation learning from human demonstrations is a promising paradigm for teaching robots
manipulation skills in the real world. However, learning complex long-horizon tasks often …

Recent work in visual representation learning for robotics demonstrates the viability of
learning from large video datasets of humans performing everyday tasks. Leveraging …

Imitation learning from human hand motion data presents a promising avenue for imbuing
robots with human-like dexterity in real-world manipulation tasks. Despite this potential …