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 …

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 …

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 …

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 …

Robot learning has emerged as a promising tool for taming the complexity and diversity of
the real world. Methods based on high-capacity models, such as deep networks, hold the …

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 …

Pre-training on Internet data has proven to be a key ingredient for broad generalization in
many modern ML systems. What would it take to enable such capabilities in robotic …

Abstract Research on Inverse Reinforcement Learning (IRL) from third-person videos has
shown encouraging results on removing the need for manual reward design for robotic …

Can we learn robot manipulation for everyday tasks, only by watching videos of humans
doing arbitrary tasks in different unstructured settings? Unlike widely adopted strategies of …