Title |
Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons
|
---|---|
Published in |
Frontiers in Neurorobotics, February 2018
|
DOI | 10.3389/fnbot.2018.00005 |
Pubmed ID | |
Authors |
Clemente Lauretti, Francesca Cordella, Anna Lisa Ciancio, Emilio Trigili, Jose Maria Catalan, Francisco Javier Badesa, Simona Crea, Silvio Marcello Pagliara, Silvia Sterzi, Nicola Vitiello, Nicolas Garcia Aracil, Loredana Zollo |
Abstract |
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 allow 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 111 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 19 | 17% |
Student > Master | 17 | 15% |
Researcher | 14 | 13% |
Student > Doctoral Student | 9 | 8% |
Student > Bachelor | 7 | 6% |
Other | 14 | 13% |
Unknown | 31 | 28% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 41 | 37% |
Computer Science | 8 | 7% |
Unspecified | 6 | 5% |
Medicine and Dentistry | 6 | 5% |
Nursing and Health Professions | 2 | 2% |
Other | 13 | 12% |
Unknown | 35 | 32% |