activity
20172023
most citedOn the Opportunities and Risks of Foundation Models

2.3k citations · 2.8k across the 22 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

cs.LG2021★ 5 cited

MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance

Michael Luo, Ashwin Balakrishna, Brijen Thananjeyan +6

Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments. Recent work learns risk measures which measure the probability of violating const…

cs.RO2021★ 12 cited

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…

cs.RO2021★ 8 cited

Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks

Bohan Wu, Suraj Nair, Li Fei-Fei +1

In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks. Reinforcement lear…

cs.LG2021★ 2.3k cited

On the Opportunities and Risks of Foundation Models

Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…

cs.CV2021★ 29 cited

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…

cs.RO2021

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…

cs.CV2021★ 7 cited

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…