Incremental Learning for Enhanced Personalization of Autocomplete Teleoperation

Mohammad Haj Hussein, Batool Ibrahim, Imad H. Elhajj, Daniel C. Asmar

Abstract: Remote controlling robots without any automated help is difficult due to various limitations. Autocomplete mitigates this difficulty by automatically detecting and completing the intended motions on robots from the input of the user. Such an approach can improve the system performance and reduce the load on the operator. Usually, recognizing intended motions is achieved using pre-trained Deep Learning (DL) models. In this paper, we introduce personalization to the autocomplete teleoperation framework when new operators take over by customizing the autocomplete DL model using incremental learning. Also, we tackle the problem of concept drift that arises in real-life applications; the data distribution of already learned classes may change in unforeseen ways as new observations of these classes come sequentially over time. We create and update an exemplar set using new observations of the classes online so that the model can be trained to adapt to the new observations. Several scenarios have been evaluated to balance the speed of learning with the accuracy of the model, and results demonstrate the effectiveness of the proposed models and their advantage in adapting to the specific operator versus our previous framework: personalization using transfer learning with full feedback.