16 citations · 79 across the 61 of their papers we have counts for
5 papers · 1 filter
Discovering and Achieving Goals via World Models
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis +2
How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision? We decompose this question into two problems: discover…
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
Frederik Ebert, Yanlai Yang, Karl Schmeckpeper +5
Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which ca…
Model-Based Reinforcement Learning via Latent-Space Collocation
Oleh Rybkin, Chuning Zhu, Anusha Nagabandi +3
The ability to plan into the future while utilizing only raw high-dimensional observations, such as images, can provide autonomous agents with broad capabilities. Visual model-base…
Birds of a Feather: Capturing Avian Shape Models from Images
Yufu Wang, Nikos Kolotouros, Kostas Daniilidis +1
Animals are diverse in shape, but building a deformable shape model for a new species is not always possible due to the lack of 3D data. We present a method to capture new species…
Object-centric Video Prediction without Annotation
Karl Schmeckpeper, Georgios Georgakis, Kostas Daniilidis
In order to interact with the world, agents must be able to predict the results of the world's dynamics. A natural approach to learn about these dynamics is through video predictio…