Tobias Jülg — accepted AI-conference papers

2026

Augmented Reality for RObots (ARRO): Pointing Visuomotor Policies Towards Visual Robustness

Reihaneh Mirjalili, Tobias Jülg, Florian Walter, Wolfram Burgard

RA-L 2026

Visuomotor policies trained on human expert demonstrations have recently shown strong performance across a wide range of robotic manipulation tasks. However, these policies remain highly sensitive to domain shifts stemming from background or robot embodiment changes, which limits their generalizatio

Cited by 8 Source ↗code ↗Scholar ↗

2025

Refined Policy Distillation: From VLA Generalists to RL Experts

Tobias Jülg, Wolfram Burgard, Florian Walter

IROS 2025

Vision-Language-Action Models (VLAs) have demonstrated remarkable generalization capabilities in real-world experiments. However, their success rates are often not on par with expert policies, and they require fine-tuning when the setup changes. In this work, we introduce Refined Policy Distillation

Cited by 15 Source ↗code ↗Scholar ↗