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 robot-object interactions. However, learning such a structured world model, which specifically captures entities and relationships, remains a challenging and underexplored problem. To address this, we propose FOCUS, a model-based agent that learns an object-centric world model. Thanks to a novel exploration bonus that stems from the object-centric representation, FOCUS can be deployed on robotics manipulation tasks to explore object interactions more easily. Evaluating our approach on manipulation tasks across different settings, we show that object-centric world models allow the agent to solve tasks more efficiently and enable consistent exploration of robot-object interactions. Using a Franka Emika robot arm, we also showcase how FOCUS could be adopted in real-world settings.
@article{arxiv.2307.02427,
title = {FOCUS: Object-Centric World Models for Robotics Manipulation},
author = {Stefano Ferraro and Pietro Mazzaglia and Tim Verbelen and Bart Dhoedt},
journal= {arXiv preprint arXiv:2307.02427},
year = {2023}
}