Designing multimodal interaction for assistive robotic arm

2007 ◽  
Author(s):  
Mossaab Hariz ◽  
Stephane Renouard ◽  
Mounir Mokhtari
Keyword(s):  
Multimodal Interaction ◽  
Robotic Arm ◽  
Assistive Robotic

Proof of Concept of an Assistive Robotic Arm Control Using Artificial Stereovision and Eye-Tracking

2019 ◽  
Vol 27 (12) ◽  
pp. 2344-2352 ◽  
Author(s):  
Yann-Seing Law-Kam Cio ◽  
Maxime Raison ◽  
Cedric Leblond Menard ◽  
Sofiane Achiche
Keyword(s):  
Eye Tracking ◽  
Robotic Arm ◽  
Proof Of Concept ◽  
Robotic Arm Control ◽  
Assistive Robotic

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

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Iason Batzianoulis ◽  
Fumiaki Iwane ◽  
Shupeng Wei ◽  
Carolina Gaspar Pinto Ramos Correia ◽  
Ricardo Chavarriaga ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real Life ◽  
Robotic Manipulators ◽  
Robotic Arm ◽  
Inverse Reinforcement Learning ◽  
Motor Disabilities ◽  
Robot Controller ◽  
Robot Behavior ◽  
Related Potentials ◽  
Assistive Robotic

AbstractRobotic 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.

Semi-Autonomous Tongue Control of an Assistive Robotic Arm for Individuals with Quadriplegia

2019 ◽  
Author(s):  
Max Hildebrand ◽  
Frederik Bonde ◽  
Rasmus Vedel Nonboe Kobborg ◽  
Christian Andersen ◽  
Andreas Flem Norman ◽  
...  
Keyword(s):  
Robotic Arm ◽  
Tongue Control ◽  
Assistive Robotic
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