6 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…
Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale
Tobias Jülg, Pierre Krack, Seongjin Bien +7
Vision-Language-Action models (VLAs) mark a major shift in robot learning. They replace specialized architectures and task-tailored components of expert policies with large-scale d…
FlowTouch: View-Invariant Visuo-Tactile Prediction
Seongjin Bien, Carlo Kneissl, Tobias Jülg +6
Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception…
VLAgents: A Policy Server for Efficient VLA Inference
Tobias Jülg, Khaled Gamal, Nisarga Nilavadi +5
The rapid emergence of Vision-Language-Action models (VLAs) has a significant impact on robotics. However, their deployment remains complex due to the fragmented interfaces and the…
Augmented Reality for RObots (ARRO): Pointing Visuomotor Policies Towards Visual Robustness
Reihaneh Mirjalili, Tobias Jülg, Florian Walter +1
Visuomotor policies trained on human expert demonstrations have recently shown strong performance across a wide range of robotic manipulation tasks. However, these policies remain…
Refined Policy Distillation: From VLA Generalists to RL Experts
Tobias Jülg, Wolfram Burgard, Florian Walter
Vision-Language-Action Models (VLAs) have demonstrated remarkable generalization capabilities in real-world experiments. However, their success rates are often not on par with expe…