1 citations · 1 across the 6 of their papers we have counts for
6 papers
Grounding Hierarchical Vision-Language-Action Models Through Explicit Language-Action Alignment
Theodor Wulff, Federico Tavella, Rahul Singh Maharjan +2
Achieving robot transparency is a critical step toward effective human-robot collaboration. To be transparent, a robot's natural language communication must be consistent with its…
Hierarchical, Interpretable, Label-Free Concept Bottleneck Model
Haodong Xie, Yujun Cai, Rahul Singh Maharjan +3
Concept Bottleneck Models (CBMs) introduce interpretability to black-box deep learning models by predicting labels through human-understandable concepts. However, unlike humans, wh…
Fake or Real, Can Robots Tell? Evaluating VLM Robustness to Domain Shift in Single-View Robotic Scene Understanding
Federico Tavella, Amber Drinkwater, Angelo Cangelosi
Robotic scene understanding increasingly relies on Vision-Language Models (VLMs) to generate natural language descriptions of the environment. In this work, we systematically evalu…
From Concrete to Abstract: A Multimodal Generative Approach to Abstract Concept Learning
Haodong Xie, Rahul Singh Maharjan, Federico Tavella +1
Understanding and manipulating concrete and abstract concepts is fundamental to human intelligence. Yet, they remain challenging for artificial agents. This paper introduces a mult…
Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation
Federico Tavella, Aphrodite Galata, Angelo Cangelosi
Artificial agents, particularly humanoid robots, interact with their environment, objects, and people using cameras, actuators, and physical presence. Their communication methods a…
A Machine Learning-based Approach to Detect Threats in Bio-Cyber DNA Storage Systems
Federico Tavella, Alberto Giaretta, Mauro Conti +1
Data storage is one of the main computing issues of this century. Not only storage devices are converging to strict physical limits, but also the amount of data generated by users…