Learning robotic skills from raw human videos remains a non-trivial challenge. Previous
works tackled this problem by leveraging behavior cloning or learning reward functions from …

Human demonstration videos are a widely available data source for robot learning and an
intuitive user interface for expressing desired behavior. However, directly extracting …

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 …

Human videos encapsulate a diverse spectrum of operational behaviors, serving as
invaluable pedagogical resources for imitation learning. In this paper, we introduce an …

Observing a human demonstrator manipulate objects provides a rich, scalable and
inexpensive source of data for learning robotic policies. However, transferring skills from …

Robotic reinforcement learning (RL) holds the promise of enabling robots to learn complex
behaviors through experience. However, realizing this promise for long-horizon tasks in the …

Robots can use Visual Imitation Learning (VIL) to learn everyday tasks from video
demonstrations. However, translating visual observations into actionable robot policies is …

Key challenges for the deployment of reinforcement learning (RL) agents in the real world
are the discovery, representation and reuse of skills in the absence of a reward function. To …

Videos of humans performing tasks are a promising data source for robotic manipulation,
because they are easy to collect in a wide range of scenarios and thus have the potential to …

Learning a generalist embodied agent capable of completing multiple tasks poses
challenges, primarily stemming from the scarcity of action-labeled robotic datasets. In …