127 citations · 148 across the 7 of their papers we have counts for
7 papers
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations
Michael Poli, Winnie Xu, Stefano Massaroli +3
Many patterns in nature exhibit self-similarity: they can be compactly described via self-referential transformations. Said patterns commonly appear in natural and artificial objec…
LISA: Learning Interpretable Skill Abstractions from Language
Divyansh Garg, Skanda Vaidyanath, Kuno Kim +2
Learning policies that effectively utilize language instructions in complex, multi-task environments is an important problem in sequential decision-making. While it is possible to…
Imitation with Neural Density Models
Kuno Kim, Akshat Jindal, Yang Song +3
We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) us…
Active World Model Learning with Progress Curiosity
Kuno Kim, Megumi Sano, Julian De Freitas +2
World models are self-supervised predictive models of how the world evolves. Humans learn world models by curiously exploring their environment, in the process acquiring compact ab…
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
Chuang Gan, Jeremy Schwartz, Seth Alter +21
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between m…
Domain Adaptive Imitation Learning
Kuno Kim, Yihong Gu, Jiaming Song +2
We study the question of how to imitate tasks across domains with discrepancies such as embodiment, viewpoint, and dynamics mismatch. Many prior works require paired, aligned demon…