Reinforcement learning techniques are widely used when robots have to learn new tasks but they typically operate on action spaces defined by the joints of the robot. We present a contrasting approach where actions spaces are the trajectories of objects in the environment, requiring robots to discover events such as object changes and behaviors that must occur to accomplish the task. We show that this allows robots to learn faster, to learn semantic representations that can be communicated to humans, and to learn in a manner that does not depend on the robot itself, enabling low-cost policy transfer between different types of robots.
@inproceedings{thierauf2024iros,
title={Fixing symbolic plans with reinforcement learning in object-based action spaces},
author={Christopher Thierauf and Matthias Scheutz},
year={2024},
booktitle={Proceedings of the International Conference on Intelligent Robots and Systems},
publisher={IEEE},
url={https://hrilab.tufts.edu/publications/thierauf2024iros.pdf}
}