Autonomous Learning of Internal Dynamic Models for Reaching Tasks

The paper addresses the problem of learning internal task-specific dynamic models for a reaching task. Using task-specific dynamic models is crucial for achieving both high tracking accuracy and compliant behaviour, which improves safety concerns while working in unstructured environment or with humans. The proposed approach uses programming by demonstration to learn new task-related movements encoded as Compliant Movement Primitives (CMPs). CMPs are a combination of position trajectories encoded in a form of Dynamic Movement Primitives (DMPs) and corresponding task-specific Torque Primitives (TPs) encoded as a linear combination of kernel functions. Unlike the DMPs, TPs cannot be directly acquired from user demonstrations. Inspired by the human sensorimotor learning ability we propose a novel method which autonomously learns task-specific TPs, based on a given kinematic trajectory in DMPs.


Editor(s):
Borangiu, T
Published in:
Advances In Robot Design And Intelligent Control, 371, 439-447
Presented at:
24th International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), Bucharest, ROMANIA, MAY 27-29, 2015
Year:
2016
Publisher:
Berlin, Springer-Verlag Berlin
ISSN:
2194-5357
ISBN:
978-3-319-21290-6
978-3-319-21289-0
Keywords:
Laboratories:




 Record created 2016-10-18, last modified 2018-05-05


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