9 papers · 1 filter
Do Vision-Language-Action Models Understand Instructions? A Mechanistic Interpretability Study on Language Grounding
Theodor Wulff, Angelo Cangelosi
Vision-Language-Action models are designed to generalise across environments and task descriptions, raising the question of whether their action generation actually depends on the…
PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learning
Hang Yu, James Staley, Cheng Xi Tsou +8
Human demonstrations provide dense policy-level information but sometimes lack local precision. Human feedback presents accurate local critiques, but offers sparse evaluations rath…
Latent Cluster Analysis for Vision-Language-Action Models
Theodor Wulff, Sergio Lanza, Tamara Bila +3
Vision-Language-Action (VLA) Models are increasingly used in robotics for their ability to ground language and perception into action, yet the internal representations driving thei…
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…
DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation
Tien Pham, Xinyun Chi, Khang Nguyen +2
Reinforcement learning (RL) agents can learn to solve complex tasks from visual inputs, but generalizing these learned skills to new environments remains a major challenge in RL ap…
LHM-Humanoid: Long-Horizon Human Motion Control for Continuous Object Transport in Cluttered Scenes
Haozhuo Zhang, Jingkai Sun, Michele Caprio +5
Physics-based human motion control can make a simulated character walk, sit, and manipulate objects with high physical realism. Almost always, though, this happens in short, isolat…
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…
Joint Action Language Modelling for Transparent Policy Execution
Theodor Wulff, Rahul Singh Maharjan, Xinyun Chi +1
An agent's intention often remains hidden behind the black-box nature of embodied policies. Communication using natural language statements that describe the next action can provid…
The ATTUNE model for Artificial Trust Towards Human Operators
Giannis Petousakis, Angelo Cangelosi, Rustam Stolkin +1
This paper presents a novel method to quantify Trust in HRI. It proposes an HRI framework for estimating the Robot Trust towards the Human in the context of a narrow and specified…