3 papers
cs.RO2026
On the Efficiency of LoRA Fine-Tuning for Vision-Language-Action Models in Industrial Robotic Manipulation
Finn Ferchau, Daniel Pommer, Cristian Axenie
Deploying billion-parameter Vision-Language-Action (VLA) models on industrial hardware requires fine-tuning to bridge the embodiment gap. Full Fine-Tuning (FFT) provides maximal pl…
cs.RO2023
Antifragile Control Systems: The case of mobile robot trajectory tracking in the presence of uncertainty
Cristian Axenie, Matteo Saveriano
Mobile robots are ubiquitous. Such vehicles benefit from well-designed and calibrated control algorithms ensuring their task execution under precise uncertainty bounds. Yet, in tas…
cs.NE2020
A Framework for Learning Invariant Physical Relations in Multimodal Sensory Processing
Du Xiaorui, Yavuzhan Erdem, Immanuel Schweizer +1
Perceptual learning enables humans to recognize and represent stimuli invariant to various transformations and build a consistent representation of the self and physical world. Suc…