Programming-by-Demonstration of reaching motions-A next-state-planner approach

Authors:
Alexander Skoglund;Boyko Iliev;Rainer Palm
Affiliations:
AASS Learning Systems Lab, Örebro Universitet, Fakultetsg. 1, SE-70182 Örebro, Sweden;AASS Learning Systems Lab, Örebro Universitet, Fakultetsg. 1, SE-70182 Örebro, Sweden;AASS Learning Systems Lab, Örebro Universitet, Fakultetsg. 1, SE-70182 Örebro, Sweden
Venue:
Robotics and Autonomous Systems
Year:
2010

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Abstract

This paper presents a novel approach to skill acquisition from human demonstration. A robot manipulator with a morphology which is very different from the human arm simply cannot copy a human motion, but has to execute its own version of the skill. When a skill once has been acquired the robot must also be able to generalize to other similar skills, without a new learning process. By using a motion planner that operates in an object-related world frame called hand-state, we show that this representation simplifies skill reconstruction and preserves the essential parts of the skill.