3 papers
cs.RO2026
Rewarding DINO: Predicting Dense Rewards with Vision Foundation Models
Pierre Krack, Tobias Jülg, Wolfram Burgard +1
Well-designed dense reward functions in robot manipulation not only indicate whether a task is completed but also encode progress along the way. Generally, designing dense rewards…
cs.RO2026
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…
cs.RO2025
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…