activity
20242026
most citedMitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2026

TAMPAgent: Agentic Belief-Space Planning for Mobile Manipulation

Utkarsh A. Mishra, Alperen Degirmenci, Yongxin Chen +3

Open-world robotic mobile manipulation requires acting in physical environments with only partial information. While recent Large Vision-Language Model (LVLM) agents show strong op…

cs.RO2026

ROSA: A Robotics Foundation Model Serving System for Robot Factories

Wenqi Jiang, Jason Clemons, Rowland O'Flaherty +5

Robotics foundation models (RFMs) are making general-purpose robots increasingly practical for factory deployments. While RFM serving systems are central to this vision, existing s…

cs.CV2026

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…

cs.RO2025

Data Scaling Laws for End-to-End Autonomous Driving

Alexander Naumann, Xunjiang Gu, Tolga Dimlioglu +7

Autonomous vehicle (AV) stacks have traditionally relied on decomposed approaches, with separate modules handling perception, prediction, and planning. However, this design introdu…

cs.RO2024★ 1 cited

Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

Alexander Popov, Alperen Degirmenci, David Wehr +9

We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a neural network capable of predicting an…