When a robot adapts a learned task for a novel environment, any changes to objects in the novel environment have an unknown effect on its task execution. For example, replacing an object in a pick-and-place task affects where the robot should target its actions, but does not necessarily affect the underlying action model. In contrast, replacing a tool that the robot will use to complete a task will effectively alter its end-effector pose with respect to the robot's base coordinate system, and thus the robot's motion must be replanned accordingly.

These examples highlight the relationship among (i) differences between the source and target environments, (ii) the level of abstraction at which a robot's task model should be represented to enable transfer to the target environment, and (iii) the information needed to ground the abstracted task representation in the target environment. In this article, we present a taxonomy of transfer problems based on this relationship. We also describe a knowledge representation called the Tiered Task Abstraction (TTA) and demonstrate its applicability to a variety of transfer problems in the taxonomy. Our experimental results indicate a trade-off between the generality and data requirements of a task representation, and reinforce the need for multiple transfer methods that operate at different levels of abstraction.

当机器人将学习到的任务适应新环境时，新环境中对象的任何变化对其任务执行都有未知的影响。例如，在拾取和放置任务中替换对象会影响机器人应将其动作瞄准的位置，但不一定会影响底层动作模型。相比之下，更换机器人用于完成任务的工具将有效地改变其末端执行器相对于机器人基础坐标系的姿态，因此必须相应地重新规划机器人的运动。

这些示例突出了 (i) 源环境和目标环境之间的差异，(ii) 应表示机器人任务模型以能够转移到目标环境的抽象级别，以及 (iii) 接地所需的信息之间的关系目标环境中的抽象任务表示。在本文中，我们提出了基于这种关系的转移问题分类法。我们还描述了一种称为分层任务抽象 (TTA) 的知识表示，并展示了它对分类法中各种转移问题的适用性。我们的实验结果表明了任务表示的通用性和数据要求之间的权衡，并加强了对在不同抽象级别运行的多种传输方法的需求。