Exploring Affordances Using Human-Guidance and Self-Exploration

Our work is aimed at service robots deployed in human environments that will need many specialized object manipulation skill. We believe robots should leverage end-users to quickly and efficiently learn the affordances of objects in their environment. Prior work has shown that this approach is promising because people naturally focus on showing salient rare aspects ofthe objects (Thomaz and Cakmak 2009). We replicate these prior results and build on them to create a semi-supervised combination of self and guided learning.We compare three conditions: (1) learning through self-exploration, (2) learning from demonstrations providedby 10 naive users, and (3) self-exploration seeded with the user demonstrations. Initial results suggests benefits of a mixed initiative approach.