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FOCUS FOCUS: Object-Centric World Models for Robotics Manipulation Stefano Ferraro*, Pietro Mazzaglia*, Tim Verbelen, Bart Dhoert * Equal contribution Article Code Abstract Understanding the world in terms of objects and the possible interactions with them is an important cognitive ability. However, current world models adopted in reinforcement learning typically lack this structure and represent the world state in a global latent vector. To address this, we propose FOCUS, a model-based agent that learns an object-centric world model. This novel representation also enables the design of an object-centric exploration mechanism, which encourages the agent to interact with objects and discover useful interactions. We benchmark FOCUS in several robotic manipulation settings, where we found that our method can be used to improve manipulation skills. The object-centric world model leads to more accurate predictions of the ...
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