Learning Complex Sequential Tasks from Demonstration: A Pizza Dough Rolling Case Study

This paper introduces a hierarchical framework that is capable of learning complex sequential tasks from human demonstrations through kinesthetic teaching, with minimal human intervention. Via an automatic task segmentation and action primitive discovery algorithm, we are able to learn both the high-level task decomposition (into action primitives), as well as low-level motion parameterizations for each action, in a fully integrated framework. In order to reach the desired task goal, we encode a task metric based on the evolution of the manipulated object during demonstration, and use it to sequence and parametrize each action primitive. We illustrate this framework with a pizza dough rolling task and show how the learned hierarchical knowledge is directly used for autonomous robot execution.

Published in:
Eleventh Acm/Ieee International Conference On Human Robot Interation (Hri'16), 611-612
Presented at:
11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Christchurch, NEW ZEALAND, MAR 07-10, 2016
New York, Assoc Computing Machinery

 Record created 2017-01-24, last modified 2018-09-13

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