(Georgia Institute of Technology, 2012-06)
Akgun, Baris; Cakmak, Maya; Jiang, Karl; Thomaz, Andrea L.
We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a human-robot interaction perspective. Our approach –Keyframe-based Learning from Demonstration (KLfD)– takes demonstrations that consist of keyframes; a sparse set of points in the state space that produces the intended skill when visited in sequence. The conventional type of trajectory demonstrations or a hybrid of the two are also handled by KLfD through a conversion to keyframes. Our method produces a skill model that consists of an ordered set of keyframe clusters, which we call Sequential Pose Distributions (SPD). The skill is reproduced by splining between clusters. We present results from two domains: mouse gestures in 2D and scooping, pouring and placing skills on a humanoid robot. KLfD has performance similar to existing LfD techniques when applied to conventional trajectory demonstrations. Additionally, we demonstrate that KLfD may be preferable when demonstration type is suited for the skill.
(Georgia Institute of Technology, 2009)
Thomaz, Andrea L.; Cakmak, Maya
A general learning task for a robot in a new environment is
to learn about objects and what actions/effects they afford.
To approach this, we look at ways that a human partner
can intuitively help the robot learn, Socially Guided Machine
Learning. We present experiments conducted with
our robot, Junior, and make six observations characterizing
how people approached teaching about objects. We show
that Junior successfully used transparency to mitigate errors.
Finally, we present the impact of “social” versus “nonsocial”
data sets when training SVM classifiers.