Entity-Centric Reinforcement Learning for Object Manipulation from Pixels

• URL: http://arxiv.org/abs/2404.01220v1
• Date: Mon, 1 Apr 2024 16:25:08 GMT
• Title: Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
• Authors: Dan Haramati, Tal Daniel, Aviv Tamar,
• Abstract summary: Reinforcement Learning (RL) offers a general approach to learn object manipulation. In practice, domains with more than a few objects are difficult for RL agents due to the curse of dimensionality. We propose a structured approach for visual RL that is suitable for representing multiple objects and their interaction.
• Score: 22.104757862869526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics. In principle, Reinforcement Learning (RL) offers a general approach to learn object manipulation. In practice, however, domains with more than a few objects are difficult for RL agents due to the curse of dimensionality, especially when learning from raw image observations. In this work we propose a structured approach for visual RL that is suitable for representing multiple objects and their interaction, and use it to learn goal-conditioned manipulation of several objects. Key to our method is the ability to handle goals with dependencies between the objects (e.g., moving objects in a certain order). We further relate our architecture to the generalization capability of the trained agent, based on a theoretical result for compositional generalization, and demonstrate agents that learn with 3 objects but generalize to similar tasks with over 10 objects. Videos and code are available on the project website: https://sites.google.com/view/entity-centric-rl

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