Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze–Brain Machine Interface

Recent developments in the non-muscular human-robot interface (HRI) and shared control strategies have shown potentials for controlling the assistive robotic arm by the people with no residual movement or muscular activity in upper limbs. However, most non-muscular HRIs only produce discrete-valued commands, resulting in non-intuitive and less effective control of dexterous assistive robotic arm. Furthermore, the user commands and the robot autonomy commands usually switch in the shared control strategies of such applications. This characteristic has been found to yield reduced sense of agency and frustration for the user according to previous user studies. In this study, we firstly propose an intuitive and easy-to-learn-and-use hybrid HRI by combing the Brain-machine interface (BMI) and the gaze-tracking interface. For the proposed hybrid gaze-BMI, the continuous modulation of the movement speed via the motor intention is simultaneous to the unconstrained movement direction control with the gaze signals, in a seamless way. We then propose a shared control paradigm that always combines user input and the autonomy with the dynamical combination regulation. The proposed hybrid gaze-BMI and shared control paradigm were validated for a robotic arm reaching task performed with healthy subjects. All the users were able to employ the hybrid gaze-BMI for moving the end-effector sequentially to reach the target across the horizontal plane, while avoiding collisions with obstacles. The shared control paradigm maintained as much volitional control as possible, while providing the assistance for the most difficult parts of the task. The presented semi-autonomous robotic system yielded continuous, smooth and collision-free motion trajectories for the end-effector approaching the target. Compared to the system without assistances from robot autonomy, it significantly reduces the failures, efforts and time for the user to complete the tasks.