2.3k citations · 2.3k across the 10 of their papers we have counts for
10 papers
Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations
Bohan Wu, Julius von Kügelgen, David M. Blei
Causal representation learning (CRL) aims to learn low-dimensional causal latent variables from high-dimensional observations. While identifiability has been extensively studied fo…
Rapid Adaptation of Particle Dynamics for Generalized Deformable Object Mobile Manipulation
Bohan Wu, Roberto Martín-Martín, Li Fei-Fei
We address the challenge of learning to manipulate deformable objects with unknown dynamics. In non-rigid objects, the dynamics parameters define how they react to interactions --…
M-EMBER: Tackling Long-Horizon Mobile Manipulation via Factorized Domain Transfer
Bohan Wu, Roberto Martin-Martin, Li Fei-Fei
In this paper, we propose a method to create visuomotor mobile manipulation solutions for long-horizon activities. We propose to leverage the recent advances in simulation to train…
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…
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.…
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…