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Using Gaussian Mixture Models for Gesture Recognition During Haptically Guided Telemanipulation

Department of Systems Engineering and Automation, Universidad de Málaga, Andalucía Tech, 29071 Málaga, Spain
Delft University of Technology, 2628 Delft, The Netherlands
Telerobotics and Haptics lab, European Space Agency, 2201 Noordwijk, The Netherlands
Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 772;
Received: 14 June 2019 / Revised: 2 July 2019 / Accepted: 4 July 2019 / Published: 10 July 2019
(This article belongs to the Special Issue Cognitive Robotics & Control)
PDF [2026 KB, uploaded 12 July 2019]


Haptic guidance is a promising method for assisting an operator in solving robotic remote operation tasks. It can be implemented through different methods, such as virtual fixtures, where a predefined trajectory is used to generate guidance forces, or interactive guidance, where sensor measurements are used to assist the operator in real-time. During the last years, the use of learning from demonstration (LfD) has been proposed to perform interactive guidance based on simple tasks that are usually composed of a single stage. However, it would be desirable to improve this approach to solve complex tasks composed of several stages or gestures. This paper extends the LfD approach for object telemanipulation where the task to be solved is divided into a set of gestures that need to be detected. Thus, each gesture is previously trained and encoded within a Gaussian mixture model using LfD, and stored in a gesture library. During telemanipulation, depending on the sensory information, the gesture that is being carried out is recognized using the same LfD trained model for haptic guidance. The method was experimentally verified in a teleoperated peg-in-hole insertion task. A KUKA LWR4+ lightweight robot was remotely controlled with a Sigma.7 haptic device with LfD-based shared control. Finally, a comparison was carried out to evaluate the performance of Gaussian mixture models with a well-established gesture recognition method, continuous hidden Markov models, for the same task. Results show that the Gaussian mixture models (GMM)-based method slightly improves the success rate, with lower training and recognition processing times. View Full-Text
Keywords: robotics; telemanipulation; haptics; machine learning; gesture recognition robotics; telemanipulation; haptics; machine learning; gesture recognition

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Pérez-del-Pulgar, C.J.; Smisek, J.; Rivas-Blanco, I.; Schiele, A.; Muñoz, V.F. Using Gaussian Mixture Models for Gesture Recognition During Haptically Guided Telemanipulation. Electronics 2019, 8, 772.

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