Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials.

Autor: Batzianoulis I; Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Iwane F; Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.; Brain-Machine Interface (CNBI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.; Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA., Wei S; Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Correia CGPR; Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Chavarriaga R; Brain-Machine Interface (CNBI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland., Millán JDR; Brain-Machine Interface (CNBI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. jose.millan@austin.utexas.edu.; Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA. jose.millan@austin.utexas.edu.; Department of Neurology, University of Texas at Austin, Austin, TX, USA. jose.millan@austin.utexas.edu., Billard A; Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. aude.billard@epfl.ch.
Jazyk: angličtina
Zdroj: Communications biology [Commun Biol] 2021 Dec 16; Vol. 4 (1), pp. 1406. Date of Electronic Publication: 2021 Dec 16.
DOI: 10.1038/s42003-021-02891-8
Abstrakt: Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. However, BCI performance may vary due to the non-stationary nature of the electroencephalogram (EEG) signals. It, hence, cannot be used safely for controlling tasks where errors may be detrimental to the user. Avoiding obstacles is one such task. As there exist many techniques to avoid obstacles in robotics, we propose to give the control to the robot to avoid obstacles and to leave to the user the choice of the robot behavior to do so a matter of personal preference as some users may be more daring while others more careful. We enable the users to train the robot controller to adapt its way to approach obstacles relying on BCI that detects error-related potentials (ErrP), indicative of the user's error expectation of the robot's current strategy to meet their preferences. Gaussian process-based inverse reinforcement learning, in combination with the ErrP-BCI, infers the user's preference and updates the obstacle avoidance controller so as to generate personalized robot trajectories. We validate the approach in experiments with thirteen able-bodied subjects using a robotic arm that picks up, places and avoids real-life objects. Results show that the algorithm can learn user's preference and adapt the robot behavior rapidly using less than five demonstrations not necessarily optimal.
(© 2021. The Author(s).)
Databáze: MEDLINE
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