Committee:

Dr. Sonia Chernova, CoC, Chair , Advisor

Dr. Mathew Gombolay, CoC

Dr. Byron Boots, CoC

Dr. Seth Hutchinson, ECE

Dr. Tucker Hermans, University of Utah

Abstract: The objective of this dissertation is to develop a family of techniques that allow robots to sample-efficiently learn diverse robot skills from human demonstrations, and subsequently generalize the skills to novel contexts while satisfying additional constraints that may exist, concerning the feasibility and coordination of robot motions. Each proposed method comes with a structured representation, suitable for tackling the challenges associated with a subset of skills. Specifically, we present: (i) a structured multi-coordinate cost learning framework coupled with an optimization routine, that generalizes skills requiring preservation of multiple geometric properties of motions, (ii) a structured prior representation employed in a probabilistic inference framework, geared towards generating optimal and feasibility-constrained motions, (iii) a stable dynamical system representation, suitable for learning skills aimed at motions that can react instantly to dynamic perturbation, and (iv) a tree-structured stable dynamical system which synthesizes multiple dynamical system into one, and learns skills dictating feasible and coordinated, yet reactive robot motions. As a preliminary to the aforementioned learning techniques, this dissertation also provides an over-arching benchmarking effort to identify the key challenges associated with skill learning from demonstration.