Abstract
We introduce “transfer by correction”: a method for transferring a robot’s tool-based task models to use unfamiliar tools. By having the robot receive corrections from a human teacher when repeating a known task with a new tool, it can learn the relationship between the two tools, allowing it to transfer additional tasks learned with the original tool to the new tool. The goal is to enable the robot to generalize its task knowledge to accommodate tool replacements and thus be more robust to changes in its environment. We demonstrate how the tool transform models learned from one episode of task corrections can be used to perform that task with >=85% of maximum performance in 83% of tool/task combinations. Furthermore, these transformations generalize to unseen tool/task combinations in 27.8% of our transfer evaluations, and up to 41% of transfer problems when the source and replacement tool share tooltip similarities. Overall, these results indicate that successful task adaptation for a new tool is dependent on the the tool’s usage within that task, and that the transform model learned from interactive corrections can be generalized to other tasks providing a similar context for the new tool.
