Abstract
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.
Highlights
In telemanipulation, a human operator performs a task in a distant environment by remotely controlling a robot
The peg-in-hole insertion task was experimentally validated by means of a telemanipulation platform located in the Telerobotics and Haptics Laboratory at the European Space Agency research center ESTEC
The defined Gaussian mixture models (GMM) gesture detection score (GGDS) was used to analyze the GMM performance taking into account the number of Gaussians, which demonstrates that the performance gets better when the number of Gaussians is increased for the peg-in-hole insertion task
Summary
A human operator performs a task in a distant environment by remotely controlling a robot. The operator needs to receive sensory information from the remote site. Depending on the received information, telemanipulation can be classified as “direct”. “uni-lateral” [1], where there is no feedback to the operator, or “bilateral” [2], which enables dual interaction between the haptic and the operator. Telemanipulation allows real-time human remote control, it is still considered to entail a rather high workload [3], at least compared with more supervisory or autonomous modes of operation. Only telemanipulation allows reacting to unknown and unforeseen situations with spontaneous feedback. Enriching telemanipulation with additional automatic assistance would allow humans to perform complex tasks more efficiently
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