# A Robot Learning Method with Physiological Interface for Teleoperation Systems
## 1 Introduction  
With significant progress in computer science, information science, automation, and artificial intelligence techniques, telerobots have been extensively applied in areas as diverse as telemedicine [1; 2], telerehabilitation [3], minimally invasive surgery [4], disaster rescue and relief operation [5], maintenance and exploration in deep sea or out space [6; 7], surgeon training [8], and telemanufacture [9], etc. Telerobots provide an alternative interactive way between the human operator and the teleoperation in order to enhance perception and motion ability of the human beings [10; 11]. It is the integration of human intelligence and the robot's advantages under the constraint of long distance [12; 13]. The performance of teleoperation largely depends on the perception of remote environmental conditions. During the recent years, many achievements indicated that some related algorithms such as impedance control, virtual fixture, and shared control, could further improve the performance of teleoperation. According to literature [14], a switched-impedance control algorithm was presentedand implemented for teleoperated heart surgery and two switched reference impedance models were designed for the master and slave robots to achieve both the stabilities of motion and force feedback. [PERSON] et al. proposed a nonlinear adaptive impedance controller with two impedance models in Cartesian space for the master and slave robots based on the consideration of dynamic uncertainties. Besides, the parameters of the impedance model were adjusted according to the objectives and requirements of the applications. This proposed adaptive impedance control method has been successfully applied in certain medical applications [3]. Furthermore, according to literature [15], a force reflection framework based on gain force observer, was developed to acquire the force information. By considering internal uncertainties and time-varying delay of the system, a satisfactory performance of the system was guaranteed for the teleoperation system. Moreover, haptic assistance method involving shared control was a choice to enhance the performance of teleoperation [16; 17; 18]. In a previous study [16], a shared-based control method was proposed for a teleoperation system and implemented in haptic shared control (HRC) and state shared control (SSC). A decision-making model with impedance control algorithm was presented to improve the tracking capability in partially unstructured environments for a teleoperation system [18]. Virtual fixture-based control algorithms were proposed to improve the performance of robot teleoperation for a cooperative task [19; 20]. In our previous study, a neural network (NN) control method with wave variable was proposed to cope with the problems of time delay and dynamics uncertainties for the teleoperation system [21]. According to literature [22], a variable gain control method with tremor attenuation was developed to adapt to the unstructured environments and to reduce the reliance on humans' operation skill. The above-mentioned algorithms could successfully deal with the problems of model uncertainty. However, they could not guarantee the improvement of efficiency of the prescribed task and release of the work pressure of the human operator. In order to improve the efficiency of the teleoperation and to reduce the workload of the human operator, many researchers have focused on how to deliver humans' skills to the robots with a short processing time and few trials. The results indicated that robot learning method could significantly improve the efficiency of the teleoperation and reduce the operational pressure for the human operator. [PERSON] proposed a learning form demonstration (LfD) framework for the learning control method [23]. [PERSON] et al. [24] proposed a remote lead through teaching (RLTT) with a dynamic time warping (DTW) method to learn the task knowledge from human demonstration [24].