Last week I attended the IEEE International Conference on Development and Learning, held at the University of Michigan. This is an interesting conference that I’ve been going to for the past few years. It’s goal is to very explicitly mingle researchers working on Machine Learning and Robotics with researchers working on understanding human learning and development.
My lab had two presentations
- “Optimality of Human Teachers for Robot Learners” (M. Cakmak, A. L. Thomaz): Here we take the notion of teaching in Machine Learning Theory, and analyze the extent to which people teaching our robot are adhering to theoretically optimal strategies. Turns out they teach about positive examples optimally, but not negative. And we can use active learning in the negative space to make up for people’s non-optimality.
- “Batch vs. Interactive Learning by Demonstration” (P. Zang, R. Tian, A.L. Thomaz, C. Isbell): We show the computational benefits of collecting LbD examples online rather than in a batch fashion. In an interactive setting people automatically improve their teaching strategy when it is sub-optimal.
And here are some cool things I learned at ICDL.
Keynote speaker, Felix Warneken, gave a really interesting talk about the origins of cooperative behavior in humans. Are people helpful and good at teamwork because you learn it, or do we have some predisposition? His work takes you through a series of great experiments with young children, showing that helping and cooperation are things we are at least partly hardwired to do.
Chen Yu, from Indiana, does some really nice research looking into how babies look around a scene, and how this is different than adults or even older children. They do this by having them wear headbands with cameras, then they can do some nice correlations across multiple video streams and audio streams to analyze the data. For younger children, visual selection is very tied to manual selection. And the success of word learning is determined by the visual dominance of the named target.
Vollmer et al, from Bielefeld, did an analysis of their motionese video corpus, and showed the different ways that a child learner gives feedback to an adult teacher. Particularly that this changes from being dominated by gaze behaviors, to more complex anticipatory gestures between the ages of 8mo to 30 mo.
Several papers touched on the topic of Intrinsic motivation for robots, as inspired by babies and other natural learners. Over the past few years there has been growing interest in this idea. People have gone from focusing on curiosity and novelty, to competence and mastery. There were papers on this topic from Barto’s lab, and from Oudeyer’s. The IM CLeVeR project was also presented, this is a large EU funded collaboration that aims to address intrinsic motivation for robots.