  Recent years have shown a growing interest in using haptic shared control
(HSC) in teleoperated systems. In HSC, the application of virtual guiding
forces decreases the user's control effort and improves execution time in
various tasks, presenting a good alternative in comparison with direct
teleoperation. HSC, despite demonstrating good performance, opens a new gap:
how to design the guiding forces. For this reason, the challenge lies in
developing controllers to provide the optimal guiding forces for the tasks that
are being performed. This work addresses this challenge by designing a
controller based on the deep deterministic policy gradient (DDPG) algorithm to
provide the assistance, and a convolutional neural network (CNN) to perform the
task detection, called TAHSC (Task Agnostic Haptic Shared Controller). The
agent learns to minimize the time it takes the human to execute the desired
task, while simultaneously minimizing their resistance to the provided
feedback. This resistance thus provides the learning algorithm with information
about which direction the human is trying to follow, in this case, the
pick-and-place task. Diverse results demonstrate the successful application of
the proposed approach by learning custom policies for each user who was asked
to test the system. It exhibits stable convergence and aids the user in
completing the task with the least amount of time possible.
