Publications (6)
Object-Centric Scene Representations using Active Inference
Toon Van de Maele, Tim Verbelen, Pietro Mazzaglia +2
Representing a scene and its constituent objects from raw sensory data is a core ability for enabling robots to interact with their environment. In this paper, we propose a novel a…
Representing Positional Information in Generative World Models for Object Manipulation
Stefano Ferraro, Pietro Mazzaglia, Tim Verbelen +2
Object manipulation capabilities are essential skills that set apart embodied agents engaging with the world, especially in the realm of robotics. The ability to predict outcomes o…
Disentangling Shape and Pose for Object-Centric Deep Active Inference Models
Stefano Ferraro, Toon Van de Maele, Pietro Mazzaglia +2
Active inference is a first principles approach for understanding the brain in particular, and sentient agents in general, with the single imperative of minimizing free energy. As…
When Object-Centric World Models Meet Policy Learning: From Pixels to Policies, and Where It Breaks
Stefano Ferraro, Akihiro Nakano, Masahiro Suzuki +1
Object-centric world models (OCWM) aim to decompose visual scenes into object-level representations, providing structured abstractions that could improve compositional generalizati…
FOCUS: Object-Centric World Models for Robotics Manipulation
Stefano Ferraro, Pietro Mazzaglia, Tim Verbelen +1
Understanding the world in terms of objects and the possible interplays with them is an important cognition ability, especially in robotics manipulation, where many tasks require r…
Symmetry and Complexity in Object-Centric Deep Active Inference Models
Stefano Ferraro, Toon Van de Maele, Tim Verbelen +1
Humans perceive and interact with hundreds of objects every day. In doing so, they need to employ mental models of these objects and often exploit symmetries in the object's shape…