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