164 citations · 175 across the 9 of their papers we have counts for
6 papers · 1 filter
Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models
Vlad Sobal, Wancong Zhang, Kyunghyun Cho +3
A long-standing goal in AI is to develop agents capable of solving diverse tasks across a range of environments, including those never seen during training. Two dominant paradigms…
Hierarchical World Models as Visual Whole-Body Humanoid Controllers
Nicklas Hansen, Jyothir S, Vlad Sobal +3
Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visu…
Gradient-based Planning with World Models
Jyothir S, Siddhartha Jalagam, Yann LeCun +1
The enduring challenge in the field of artificial intelligence has been the control of systems to achieve desired behaviours. While for systems governed by straightforward dynamics…
A Cookbook of Self-Supervised Learning
Randall Balestriero, Mark Ibrahim, Vlad Sobal +16
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art wi…
Joint Embedding Predictive Architectures Focus on Slow Features
Vlad Sobal, Jyothir S, Siddhartha Jalagam +3
Many common methods for learning a world model for pixel-based environments use generative architectures trained with pixel-level reconstruction objectives. Recently proposed Joint…
Light-weight probing of unsupervised representations for Reinforcement Learning
Wancong Zhang, Anthony GX-Chen, Vlad Sobal +2
Unsupervised visual representation learning offers the opportunity to leverage large corpora of unlabeled trajectories to form useful visual representations, which can benefit the…