Demonstration, Tactile Correction and Multiple Training Data Sources for Robot Motion Control

This work considers our approach to robot motion control learning from the stand-point of multiple data sources. Our paradigm derives data from human teachers providing task demonstrations and tactile corrections for policy refinement and reuse. We contribute a novel formalization for this data, and identify future direc-tions for the algorithm to reason explicitly about differences in data source.