@inproceedings{
haramati2023entitycentric,
title={Entity-Centric Reinforcement Learning for Object Manipulation from Pixels},
author={Dan Haramati and Tal Daniel and Aviv Tamar},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=dJvx3VtwUl}
}
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, and demonstrate agents that learn with 3 objects but generalize to similar tasks with over 10 objects. Rollout videos are available on our website: https://sites.google.com/view/entity-centric-rl
Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
Dan Haramati Tal Daniel Aviv Tamar