Online Behavior Modification for Expressive User Control of RL-Trained Robots
| Autor: | Sheidlower, Isaac, Murdock, Mavis, Bethel, Emma, Aronson, Reuben M., Short, Elaine Schaertl |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3610977.3634947 |
| Popis: | 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. Comment: This work was published and presented at HRI 2024 |
| Databáze: | arXiv |
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