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Movement primitives have the property to accommodate changes in the robotstate while maintaining attraction to the original policy. As such, weinvestigate the use of primitives as a blending mechanism by considering thatstate deviations from the original policy are caused by user inputs. As theprimitive recovers from the user input, it implicitly blends human and robotpolicies without requiring their weightings -- referred to as arbitration. Inthis paper, we adopt Dynamical Movement Primitives (DMPs), which allow us toavoid the need for multiple demonstrations, and are fast enough to enablenumerous instantiations, one for each hypothesis of the human intent. Userstudies are presented on assisted teleoperation tasks of reaching multiplegoals and dynamic obstacle avoidance. Comparable performance to conventionalteleoperation was achieved while significantly decreasing human intervention,often by more than 60%.

Blending Primitive Policies in Shared Control for Assisted Teleoperation