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Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons. | LitMetric

AI Article Synopsis

  • The paper presents a new motion planning system for upper-limb exoskeletons, using Learning by Demonstration to help patients perform daily activities in varied environments while meeting anthropomorphic criteria.
  • The system combines learning techniques and Dynamic Motion Primitives to create tailored joint trajectories for specific tasks and patients, validated with both simulations and real-life testing on patients with Limb Girdle Muscular Dystrophy.
  • Results show a 100% success rate in performing tasks like drinking and lifting, and the system maintains a human-like configuration throughout the robot's workspace.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829101PMC
http://dx.doi.org/10.3389/fnbot.2018.00005DOI Listing

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