An assistive decision-and-control architecture for force-sensitive, hand-arm systems driven via human-machine interfaces (MM2)

This video shows a 2-D pick and place of an object using the Braingate2 neural interface. 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.