2 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
An Empirical Study and Open Testbed for Federated Fine-Tuning of Vision-Language-Action Models
Zhekai Duan, Kevin Ziyang Xie, Xinyu Tan +5
Adapting a pretrained Vision-Language-Action (VLA) model to a new robot, environment, or task requires demonstrations that are collected locally and often discarded. Federated lear…
NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
NVIDIA, :, Aarti Basant +33
As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving pol…
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…
DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models
Xinglong Sun, Kevin Xie, Jenny Schmalfuss +5
Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-d…
VLM-AutoDrive: Post-Training Vision-Language Models for Safety-Critical Autonomous Driving Events
Mohammad Qazim Bhat, Yufan Huang, Niket Agarwal +7
The rapid growth of ego-centric dashcam footage presents a major challenge for detecting safety-critical events such as collisions and near-collisions, scenarios that are brief, ra…