3 papers
cs.AI2026
LUMINA: Long-horizon Understanding for Multi-turn Interactive Agents
Amin Rakhsha, Thomas Hehn, Pietro Mazzaglia +3
Large language models can perform well on many isolated tasks, yet they continue to struggle on multi-turn, long-horizon agentic problems that require skills such as planning, stat…
cs.RO2025
From Code to Action: Hierarchical Learning of Diffusion-VLM Policies
Markus Peschl, Pietro Mazzaglia, Daniel Dijkman
Imitation learning for robotic manipulation often suffers from limited generalization and data scarcity, especially in complex, long-horizon tasks. In this work, we introduce a hie…
cs.RO2025
Focusing on What Matters: Object-Agent-centric Tokenization for Vision Language Action models
Rokas Bendikas, Daniel Dijkman, Markus Peschl +2
Vision-Language-Action (VLA) models offer a pivotal approach to learning robotic manipulation at scale by repurposing large pre-trained Vision-Language-Models (VLM) to output robot…