5 citations · 10 across the 3 of their papers we have counts for
7 papers
iPack: Intuitive Bin Packing with Large Language Models
Yannik Blei, Michael Krawez, Adrian Göà +5
Robotics and automation are increasingly influential in logistics but remain largely confined to traditional warehouses. In grocery retail, advancements such as cashier-less superm…
Lan-grasp: Using Large Language Models for Semantic Object Grasping and Placement
Reihaneh Mirjalili, Michael Krawez, Yannik Blei +3
In this paper, we propose Lan-grasp, a novel approach towards more appropriate semantic grasping and placing. We leverage foundation models to equip the robot with a semantic under…
End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization
Guangming Wang, Yu Zheng, Yuxuan Wu +5
Robot localization using a built map is essential for a variety of tasks including accurate navigation and mobile manipulation. A popular approach to robot localization is based on…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Learning Continuous Control with Geometric Regularity from Robot Intrinsic Symmetry
Shengchao Yan, Baohe Zhang, Yuan Zhang +2
Geometric regularity, which leverages data symmetry, has been successfully incorporated into deep learning architectures such as CNNs, RNNs, GNNs, and Transformers. While this conc…
Collaborative Dynamic 3D Scene Graphs for Automated Driving
Elias Greve, Martin Büchner, Niclas Vödisch +2
Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building a…