It is crucial that users are empowered to use the functionalities of a robot to creatively solve problems on the fly. A user who has access to a Reinforcement Learning (RL) based robot may want to use the robot's autonomy and their knowledge of its behavior to complete new tasks. One way is for the user to take control of some of the robot's action space through teleoperation while the RL policy simultaneously controls the rest. However, an out-of-the-box RL policy may not readily facilitate this. For example, a user's control may bring the robot into a failure state from the policy's perspective, causing it to act in a way the user is not familiar with, hindering the success of the user's desired task. In this work, we formalize this problem and present Imaginary Out-of-Distribution Actions, IODA, an initial algorithm for addressing that problem and empowering user's to leverage their expectation of a robot's behavior to accomplish new tasks.

用户使用强化学习机器人的功能来创造性地解决问题非常重要，本文提出了Imaginary Out-of-Distribution Actions（IODA）算法，以解决用户使用机器人行为知识完成新任务时遇到的问题。

通过想象行为修改强化学习策略：可预测的策略如何帮助用户完成新任务