Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
<|reference_start|>Entity-Centric Reinforcement Learning for Object Manipulation from Pixels: 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<|reference_end|>
@article{haramati2024entity-centric,
    title={Entity-Centric Reinforcement Learning for Object Manipulation from
  Pixels},
    author={Dan Haramati, Tal Daniel, Aviv Tamar},
    journal={arXiv preprint arXiv:2404.01220},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01220},
    primaryClass={cs.RO cs.CV cs.LG}
}