## 1 Introduction  
Robots are being used increasingly in activities in our daily lives; thus, robots need to interact with people who are not experts in robotics. To make robots to be used conveniently and efficiently, good human-robot interaction plays an important role. Human-robot interaction (HRI) based on command lines requires that a technician operates the robot. Although HRI based on the graphical user interface has made this possible for non-expert users, it does not satisfy the requirements of natural interaction. To solve this problem, the means that humans employ to communicate with each other are introduced into human-computer interaction [1]. Humans obtain information through vision and hearing and can communicate with one another. Robots have similar capabilities: they can acquire information through visual and auditory sensors, analyze the data, and hence interact with humans naturally. In daily life, people usually communicate with one another using language and gestures and choose an adaptable manner of communicating depending on the task and objective at hand. To render human-robot interaction natural, some researchers have used hand postures/gestures and natural language to interact with robots. Vision-based hand gestures can be generally classified into static gestures (hand postures) and dynamic gestures (hand gestures) [2]. Hand shapes are hand postures (static gestures), while hand movements are hand gestures (dynamic gestures) [3]. Some researchers utilized dynamic gestures (hand gestures) and natural language to interact with robots. [PERSON] et al. [4; 5; 6; 7; 8; 9] built a multimodal system for human-robot interaction based on the humanoid robots ARMAR II and ARMAR III. [PERSON] et al. [10] used speech and hand gestures to control the movement of the mobile robot Jido. [PERSON] et al. [11] integrated voice, hand motions and body posture into a multimodal interface by using a deep learning-based method to control an industrial robot. However, some researchers used static gesture (hand postures) and speech to control movements of robots. A multimodal system of interaction has been proposed for generating a map of the environment, where hand postures and natural language are used to help a wheeled mobile robot to generate a map of the environment [12]. Hand postures and speech recognition are used to command the assistant robot ALBERT to perform simple tasks [13]. Hand postures and natural language were used to assist a lay user in a pick-and-place application [14; 15]. We focus the work that use hand postures and voice commands to control movements of the robot. In this work, we focus on tasks of reconnaissance, rescue and other public security applications, based on characteristics of our integrated leg-arm hexapod robot. At present, robots can achieve known tasks in a structural environment autonomously, while they cannot fulfill unknown tasks in an unstructured environment autonomously. However, unknown tasks in an unstructured environment are often accomplished by public security personnel. In this case, human knowledge and experiences are utilized, a supervised pattern and demonstration pattern are combined, and a method of linkage of movement and manipulation of robots is proposed based on the visual and auditory channels. Our robot has multiple ways of movement, such as \"3 + 3\" gait, \"2 + 4\" gait, and \"1 + 5\" gait. Additionally, the robot has two manipulators, and different tools can be installed in the end effectors of the manipulators, such as a clamp and scissors. Thus, it requires multiple interactivities. Moreover, it needs natural interaction between a human and the robot. The difficulty of linkage of multiple movements and manipulations of the robot is how to design an interaction system based on hand postures and speech recognition, so that public security personnel can perform tasks naturally, conveniently and efficiently.