# A Robot Learning Method with Physiological Interface for Teleoperation Systems
## 1 Introduction
Moreover, a hidden semi-Markov model (HSMM) was presented to learn a task model for the purpose of offering assistance for remote operation tasks [25]. A probabilistic method was developed to construct a task model in the assistive teleoperated operation. The performance of the teleoperation was improved by the method and demonstrated by remotely operated vehicle (ROV) tasks [26; 27]. In order to simplify the complexity of the system and to cope up with the varying dynamical interaction, [PERSON] et al. [28] developed a hierarchical interactive learning (HIL) algorithm with dynamic movement primitives (DMPs) and locally weighted regression (LWR) to learn the task trajectories for an exoskeleton system. Furthermore, researchers also employed the human-in-the-loop method with shared control and LWR for robot learning online in the non-trivial task [29; 30]. [PERSON] et al. utilized Gaussian mixture model (GMM) with DMP model to encode the demonstrated trajectories in peg-in-hole tasks. The experimental results demonstrated that the developed method could cope up with the problem of teleoperation tasks with large variabilities [31]. [PERSON] et al. [32] investigated the path smoothing algorithms for the robot navigation task. A novel human-exoskeleton interface was proposed [33] to demonstrate the performance of cable driven arm exoskeleton (CAREX) with a cable tension planner for smooth motion. The above-mentioned robot learning methods could successfully improve the work efficiency of the teleoperation by learning human skills. However, these algorithms rarely consider the relationship between remote environments and the human operator operational characteristic in the process of human-robot interaction (HRI). In a specific task with HRI, a human operator can adjust his stiffness of upper limb to deal with external disturbance. For example, the human operator can increase his stiffness or force when the resistance force of external environment gets larger in a natural way. Inspired by this, we tend to transfer the human stiffness to the robot in a real-time way enhance its interactive abilities. To be specific, electromyography (EMG) signals represent the comprehensive impact of motor unit action potentials (MAUP) of the muscle fiber both in time and in space. The EMG signals have the following three applications: indicator of the muscle activation, representation of force generated by the human muscle, and a viewer of the fatigue for the human muscle. As an indicator of the muscle activation, the surface electromyography (sEMG) signal includes more information such as force and stiffness related to human motion control information. Therefore, we extract the stiffness from the sEMG signals and transfer it to the slave device of the teleoperation system. EMG-based technologies are widely used for the robotics. [PERSON] et al. [34] proposed an sEMG-based HRI interface with pattern recognition and factorization methods to provide a natural interaction between humans and robots. Furthermore, a dynamic switching method [35] was presented to predict human operator's behavior and to provide the most effective control according to the given context or the specific task in myoelectric training tool (MTT) platform. [PERSON] et al. embedded a human as a part of the controller in the system to provide a myoelectric interface in different control tasks. The method was verified through the experiments involving the muscle activation of human upper limb [36]. According to literature [37], an HRI control interface was developed by integration of EMG signals and different functionally effective muscle (FEM) methods for a robotic wheelchair. A position-independent decoding movement intention method based on proportional myoelectric interface was proposed for the purpose of estimation of possibility of various arm positions [38]. It was concluded that the muscle activation could be used as an interaction tool to capture the information details of the remote operation environments.