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Autonomous Learning of Internal Dynamic Models for Reaching Tasks

  • Tadej PetričEmail author
  • Aleš Ude
  • Auke J. Ijspeert
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 371)

Abstract

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.

Keywords

Compliant movement primitives Task-specific dynamics Learning Dynamic movement primitives 

Notes

Acknowledgments

The research activities leading to the results presented in this paper were supported by the Sciex-NMSCH project no. 14.069.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Biorobotics LaboratoryEPFL, École Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.JSI, Jozef Stefan InstituteLjubljanaSlovenia

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