Original Research ARTICLE
Learning-by-demonstration for motion planning of upper-limb exoskeletons
- 1Università Campus Bio-Medico, Italy
- 2The BioRobotics Institute, Sant'Anna School of Advanced Studies, Italy
- 3Universidad Miguel Hernández de Elche, Spain
- 4Associazione Nazionale dei Centri Ausili (GLIC), Italy
- 5Università Campus Bio-Medico, Italy
The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace.
This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allows successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories.
System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere.
The achieved results showed a 100 % success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured.
Keywords: motion planning, machine learning, Learning by demonstration, dynamics movement primitives, assistive robotics
Received: 27 Jul 2017;
Accepted: 31 Jan 2018.
Edited by:Shuai Li, Hong Kong Polytechnic University, Hong Kong
Reviewed by:Leslie S. Smith, University of Stirling, United Kingdom
Yuqing Lin, Beijing Institute of Technology, China
Long Jin, Lanzhou University, China
Copyright: © 2018 Lauretti, Cordella, Ciancio, Trigili, Catalan, Badesa, Crea, Pagliara, Sterzi, Vitiello, Garcia-Aracil and Zollo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Clemente Lauretti, Università Campus Bio-Medico, Rome, Italy, firstname.lastname@example.org