A Case-Based Framework for Task Demonstration Storage and Adaptation

We address the problem of imitation learning in interactive robots which learn from task demonstrations. Many current approaches to interactive robot learning are performed over a set of demonstrations, where the robot observes several demonstrations of the same task and then creates a generalized model. In contrast, we aim to enable a robot to learn from individual demonstrations, each of which are stored in the robot’s memory as source cases. When the robot is later tasked with repeating a task in a new environment containing a different set of objects, features, or a new object configuration, the robot would then use a case-based reasoning framework to retrieve, adapt, and execute the source case demonstration in the new environment. We describe our ongoing work to implement this case-based framework for imitation learning in robotic agents.
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