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.

FOCUS 是一个基于模型的代理人，通过一种新颖的探索奖励机制可以在机器人操纵任务中更轻松地探索物体交互，在不同环境中的操作任务中表现出更高的效率，同时可以应用于现实世界任务中，这将有助于提高机器人的操作能力。