7 papers
MambaAct: Efficient Mamba Action Experts for Flow-Matching Vision-Language-Action Models
Khuyen Pham, Tran Nguyen Le, Edoardo Fazzari +1
Vision-language-action (VLA) models have shown strong promise for generalist robotic manipulation, but their inference cost remains a major bottleneck for real-time control. This l…
GVLA: Geometric Inductive Bias for Vision-Language-Action Models
Yue Peng, Yongzhe Zhao, Artur Habuda +5
Vision-language-action (VLA) models have rapidly advanced generalist robot manipulation by harnessing the semantic knowledge of pretrained vision-language backbones. However, their…
Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha +18
Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose pro…
FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation
Duc Minh Nguyen, Nghiem Tuong Diep, Binh Gia Nguyen +20
Vision-Language-Action (VLA) models enable general-purpose robotic control via large-scale multimodal pretraining, yet their effectiveness under few-shot imitation learning remains…
Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control
Julian Quick, Marcus Binder Nilsen, Andreas Bechmann +2
Plant-level control is an emerging wind energy technology that presents opportunities and challenges. By controlling turbines in a coordinated manner via a central controller, it i…
IA-VLA: Input Augmentation for Vision-Language-Action models in settings with semantically complex tasks
Eric Hannus, Miika Malin, Tran Nguyen Le +1
Vision-language-action models (VLAs) have become an increasingly popular approach for addressing robot manipulation problems in recent years. However, such models need to output ac…
Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints
Eric Hannus, Tran Nguyen Le, David Blanco-Mulero +1
Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such…