An assistive, decision-and-control architecture for force-sensitive, hand–arm systems driven by human–machine interfaces (MM3)

This video shows a 3-D reach and grasp experiment using the Braingate2 neural interface system. The robot is controlled through a multipriority Cartesian impedance controller and its behavior is extended with collision detection and reflex reaction. Furthermore, virtual workspaces are added to ensure safety. On top of this a decision-and-control architecture, which uses sensory information available from the robotic system to evaluate the current state of task execution, is employed. Available assistive skills of the robotic system are not actively helping in this task but they are used to evaluate task success.