Learning from visual data opens the potential to accrue a large range of manipulation
behaviors by leveraging human demonstrations without specifying each of them mathe …

Imitation learning is an effective and safe technique to train robot policies in the real world
because it does not depend on an expensive random exploration process. However, due to …

Imitation learning is a popular approach for teaching motor skills to robots. However, most
approaches focus on extracting policy parameters from execution traces alone (ie, motion …

Learning from human demonstrations is an emerging trend for designing intelligent robotic
systems. However, previous methods typically regard videos as instructions, simply dividing …

While large-scale sequence modeling from offline data has led to impressive performance
gains in natural language and image generation, directly translating such ideas to robotics …

We study the problem of learning a range of vision-based manipulation tasks from a large
offline dataset of robot interaction. In order to accomplish this, humans need easy and …

We tackle the problem of learning complex, general behaviors directly in the real world. We
propose an approach for robots to efficiently learn manipulation skills using only a handful of …

A long-standing goal in robotics is to build robots that can perform a wide range of daily
tasks from perceptions obtained with their onboard sensors and specified only via natural …

Imitation learning from human demonstrations can teach robots complex manipulation skills,
but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) …

We present a framework for robot skill acquisition, which 1) efficiently scale up data
generation of language-labelled robot data and 2) effectively distills this data down into a …