  In harsh environments such as those found in nuclear facilities, the use of
robotic systems is crucial for performing tasks that would otherwise require
human intervention. This is done to minimize the risk of human exposure to
dangerous levels of radiation, which can have severe consequences for health
and even be fatal. However, the telemanipulation systems employed in these
environments are becoming increasingly intricate, relying heavily on
sophisticated control methods and local master devices. Consequently, the
cognitive burden on operators during labor-intensive tasks is growing. To
tackle this challenge, operator intention detection based on task learning can
greatly enhance the performance of robotic tasks while reducing the reliance on
human effort in teleoperation, particularly in a glovebox environment. By
accurately predicting the operator's intentions, the robot can carry out tasks
more efficiently and effectively, with minimal input from the operator. In this
regard, we propose the utilization of Convolutional Neural Networks, a machine
learning approach, to learn and forecast the operator's intentions using raw
force feedback spatiotemporal data. Through our experimental study on glovebox
tasks for nuclear applications, such as radiation survey and object grasping,
we have achieved promising outcomes. Our approach holds the potential to
enhance the safety and efficiency of robotic systems in harsh environments,
thus diminishing the risk of human exposure to radiation while simultaneously
improving the precision and speed of robotic operations.
