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.
Recent News
New Podcast on Jill Watson and SAMI
Hiring a full-time research scientist and a half-time post-doc
News coverage on Jill Watson: what different sectors can teach us about AI
Congratulations to DILab alumni Mukundan Kuthalam for his recent acceptance to the Computer Science PhD program at Northwestern University!
Congratulations to DILab alumni Varsha Achar for starting her new job at Facebook!