6 citations · 16 across the 14 of their papers we have counts for
6 papers · 1 filter
Cross-Embodiment Robot Foundation World Models with Latent Actions
Huang Huang, Sriram Yenamandra, Arjun Majumdar +9
The diversity of robot embodiments and action spaces makes it challenging to build robot world models that generalize across different embodiments. We introduce the Latent Action-C…
H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning
Wancong Zhang, Basile Terver, Michael Rabbat +2
Long-horizon planning with latent world models requires reasoning across timescales and levels of abstraction. Existing task-agnostic JEPA world models predict and plan at a single…
V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
Lorenzo Mur-Labadia, Matthew Muckley, Amir Bar +6
We present V-JEPA 2.1, a family of self-supervised models that learn dense, high-quality visual representations for both images and videos while retaining strong global scene under…
A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures
Basile Terver, Randall Balestriero, Megi Dervishi +8
We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEPAs). JEPAs learn to predict in represent…
Parallel Stochastic Gradient-Based Planning for World Models
Michael Psenka, Michael Rabbat, Aditi Krishnapriyan +2
World models simulate environment dynamics from raw sensory inputs like video. However, using them for planning can be challenging due to the vast and unstructured search space. We…
Learning Latent Action World Models In The Wild
Quentin Garrido, Tushar Nagarajan, Basile Terver +3
Agents capable of reasoning and planning in the real world require the ability of predicting the consequences of their actions. While world models possess this capability, they mos…