Intelligent Mode-switching Framework for Teleoperation
Burak Kizilkaya Changyang She Guodong Zhao Muhammad Ali Imran
Teleoperation can be very difficult due to limited perception, high
communication latency, and limited degrees of freedom (DoFs) at the operator
side. Autonomous teleoperation is proposed to overcome this difficulty by
predicting user intentions and performing some parts of the task autonomously
to decrease the demand on the operator and increase the task completion rate.
However, decision-making for mode-switching is generally assumed to be done by
the operator, which brings an extra DoF to be controlled by the operator and
introduces extra mental demand. On the other hand, the communication
perspective is not investigated in the current literature, although
communication imperfections and resource limitations are the main bottlenecks
for teleoperation. In this study, we propose an intelligent mode-switching
framework by jointly considering mode-switching and communication systems. User
intention recognition is done at the operator side. Based on user intention
recognition, a deep reinforcement learning (DRL) agent is trained and deployed
at the operator side to seamlessly switch between autonomous and teleoperation
modes. A real-world data set is collected from our teleoperation testbed to
train both user intention recognition and DRL algorithms. Our results show that
the proposed framework can achieve up to 50% communication load reduction with
improved task completion probability.