Contrastive Learning has recently received interest due to its success in self-supervised
representation learning in the computer vision domain. However, the origins of Contrastive …

We approach the problem of learning by watching humans in the wild. While traditional
approaches in Imitation and Reinforcement Learning are promising for learning in the real …

This paper proposes a simple self-supervised approach for learning a representation for
visual correspondence from raw video. We cast correspondence as prediction of links in a …

Natural language is perhaps the most flexible and intuitive way for humans to communicate
tasks to a robot. Prior work in imitation learning typically requires each task be specified with …

Large-scale multi-task robotic manipulation systems often rely on text to specify the task. In
this work, we explore whether a robot can learn by observing humans. To do so, the robot …

Natural language is perhaps the most versatile and intuitive way for humans to communicate
tasks to a robot. Prior work on Learning from Play (LfP)(Lynch et al., 2019) provides a simple …

Contrastive self-supervised learning has shown impressive results in learning visual
representations from unlabeled images by enforcing invariance against different data …

Over the years various methods have been proposed for the problem of object detection.
Recently, we have witnessed great strides in this domain owing to the emergence of …

The ability to specify robot commands by a non-expert user is critical for building generalist
agents capable of solving a large variety of tasks. One convenient way to specify the …

Imitation learning has proven to be a powerful tool for training complex visuo-motor policies.
However, current methods often require hundreds to thousands of expert demonstrations to …