Skill Learning in Robot-Assisted Micro-Manipulation Through Human Demonstrations with Attention Guidance

For the development of robotic systems for micromanipulation, it is challenging to design appropriate control strategies due to either the lack of sufficient information for feedback or the difficulty in extracting subtle yet critical visual features. With the same system under the teleoperated mode, however, human operators seem to be able to complete the task more successfully with an inherent motion and control strategy. The extraction of implicit human attention during the task and integration of this with robot control could provide crucial guidance in the design of feature extraction and motion control algorithms. In this paper, a micro-assembly task of miniature thin membrane sensors is considered. For human demonstrations, we collected data from repeated tests performed by ten operators following three motion strategies. The human attention during the task is explored according to the coordinates of the eye gaze, and then a neural network with gaze-guided attention is trained to segment the visual Region of Interest (ROI). After quantitative evaluation of operator results in terms of success rate, efficiency, reset time, and the Index of Pupillary Activity (IPA), an optimized motion strategy based on the "palpation" framework was derived. Consequently, we apply this strategy to automated tasks and achieve superior results than human operators, showing an average task completion time of 34.8 +/- 5.9s and a success rate of over 90%.