Entity-Centric Reinforcement Learning for Object Manipulation from Pixels

Dan Haramati, Tal Daniel, Aviv Tamar

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Keywords: deep reinforcement learning, visual reinforcement learning, object-centric, robotic object manipulation, compositional generalization

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TL;DR: We introduce a structured visual goal-conditioned RL approach for multi-object manipulation, showcasing agents that generalize from 3 to over 10 objects.

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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Primary Area: reinforcement learning

Submission Number: 3597