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 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 …

Reinforcement learning is a powerful framework for robots to acquire skills from experience,
but often requires a substantial amount of online data collection. As a result, it is difficult to …

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 …

Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient
robot learning from visual observations. Yet the current approaches typically train a single …

Robots operating in human-centered environments should have the ability to understand
how objects function: what can be done with each object, where this interaction may occur …

We are motivated by the goal of generalist robots that can complete a wide range of tasks
across many environments. Critical to this is the robot's ability to acquire some metric of task …

We propose a self-supervised approach for learning representations and robotic behaviors
entirely from unlabeled videos recorded from multiple viewpoints, and study how this …

We present a large-scale study of imitating human demonstrations on tasks that require a
virtual robot to search for objects in new environments-(1) ObjectGoal Navigation (eg'find & …

To build general robotic agents that can operate in many environments, it is often imperative
for the robot to collect experience in the real world. However, this is often not feasible due to …