144 citations · 289 across the 9 of their papers we have counts for
14 papers
Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning
Maximilian Du, Olivia Y. Lee, Suraj Nair +1
Humans are capable of completing a range of challenging manipulation tasks that require reasoning jointly over modalities such as vision, touch, and sound. Moreover, many such task…
Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation
Suraj Nair, Eric Mitchell, Kevin Chen +3
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 eff…
FitVid: Overfitting in Pixel-Level Video Prediction
Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair +3
An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally repr…
Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos
Annie S. Chen, Suraj Nair, Chelsea Finn
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 o…
Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction
Bohan Wu, Suraj Nair, Roberto Martin-Martin +2
A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. However, while…
Model-Based Visual Planning with Self-Supervised Functional Distances
Stephen Tian, Suraj Nair, Frederik Ebert +4
A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-…