title: Using machine learning to blend human and robot controls for assisted wheelchair navigation.
abstract: This work presents an algorithm for collaborative control of an assistive semi-autonomous wheelchair. Our approach is based on a statistical machine learning technique to learn task variability from demonstration examples. The algorithm has been developed in the context of shared-control powered wheelchairs that provide assistance to individuals with impairments that affect their control in challenging driving scenarios, like doorway navigation. We validate our algorithm within a simulation environment, and find that with relatively few demonstrations, our approach allows for safe traversal of the doorway while maintaining a high level of user control.
authors.forename: Aditya
authors.initials: A
authors.lastname: Goil
authors.forename: Matthew
authors.initials: M
authors.lastname: Derry
authors.forename: Brenna D
authors.initials: BD
authors.lastname: Argall
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004867:Equipment Design; D008328:Man-Machine Systems; D012371:Robotics / Q000295:instrumentation*; D014910:Wheelchairs