Online Behavior Modification for Expressive User Control of RL-Trained Robots
<|reference_start|>Online Behavior Modification for Expressive User Control of RL-Trained Robots: Reinforcement Learning (RL) is an effective method for robots to learn tasks. However, in typical RL, end-users have little to no control over how the robot does the task after the robot has been deployed. To address this, we introduce the idea of online behavior modification, a paradigm in which users have control over behavior features of a robot in real time as it autonomously completes a task using an RL-trained policy. To show the value of this user-centered formulation for human-robot interaction, we present a behavior diversity based algorithm, Adjustable Control Of RL Dynamics (ACORD), and demonstrate its applicability to online behavior modification in simulation and a user study. In the study (n=23) users adjust the style of paintings as a robot traces a shape autonomously. We compare ACORD to RL and Shared Autonomy (SA), and show ACORD affords user-preferred levels of control and expression, comparable to SA, but with the potential for autonomous execution and robustness of RL.<|reference_end|>
@article{sheidlower2024online,
    title={Online Behavior Modification for Expressive User Control of RL-Trained
  Robots},
    author={Isaac Sheidlower, Mavis Murdock, Emma Bethel, Reuben M. Aronson,
  Elaine Schaertl Short},
    journal={arXiv preprint arXiv:2408.16776},
    year={2024},
    doi={10.1145/3610977.3634947},
    archivePrefix={arXiv},
    eprint={2408.16776},
    primaryClass={cs.RO cs.AI}
}