The effect of learning by imitation on a multi-robot system based on the coupling of low-level imitation strategy and online learning for cognitive map building

Abstract

It is assumed that future robots must coexist with human beings and behave as their companions. Consequently, the complexities of their tasks would increase. To cope with these complexities, scientists are inclined to adopt the anatomical functions of the brain for the mapping and the navigation in the field of robotics. While admitting the continuous works in improving the brain models and the cognitive mapping for robots' navigation, we show, in this paper, that learning by imitation leads to a positive effect not only in human behavior but also in the behavior of a multi-robot system. We present the interest of low-level imitation strategy at individual and social levels in the case of robots. Particularly, we show that adding a simple imitation capability to the brain model for building a cognitive map improves the ability of individual cognitive map building and boosts sharing information in an unknown environment. Taking into account the notion of imitative behavior, we also show that the individual discoveries (i.e. goals) could have an effect at the social level and therefore inducing the learning of new behaviors at the individual level. To analyze and validate our hypothesis, a series of experiments has been performed with and without a low-level imitation strategy in the multi-robot system.

Research on decoupling control of single leg joints of hydraulic quadruped robot

“…During the movement, the movement of the hydraulic quadruped robot is a kind of non-high-speed movement; therefore, there is no influence of Coriolis force and centripetal between joints, but the inertial force and gravity have a more significant impact on the movement between joints, so only the inertial force is considered. The effects of pressure and gravity ignore those of Coriolis and centripetal forces (Chatty et al, 2014). The leg dynamics model of the hydraulic robot is shown below:…”

“…During the movement, the movement of the hydraulic quadruped robot is a kind of non-high-speed movement; therefore, there is no influence of Coriolis force and centripetal between joints, but the inertial force and gravity have a more significant impact on the movement between joints, so only the inertial force is considered. The effects of pressure and gravity ignore those of Coriolis and centripetal forces (Chatty et al , 2014). The leg dynamics model of the hydraulic robot is shown below: Therefore, the dynamic connection between the output of two joints and the displacement of the hydraulic system is as follows: In the formula, F = [ F 2 , F 3 ] T is the output force of the hydraulic cylinder corresponding to the hip and knee joints.…”

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

“…It considered exploration as a Markov decision process and uses memory-based deep reinforcement learning as in Figure 17 a; it had further potential to reduce the search status of robots. Chatty et al [ 139 ] designed a learning-by-imitation method for a multirobot system, building a cognitive map by coupling a low-level imitation strategy. It had a positive effect on the behaviors of human and multirobot systems and on sharing information and individual cognitive map building in an unknown environment.…”

“… ( a ) The workflow of memory-based deep reinforcement learning [ 12 ]. ( b ) The construction of place cells on the cognitive map [ 139 ]. …”

A biological-inspired episodic cognitive map building framework for mobile robot navigation

“…Cognitive map, 2-3 a map-like representation, which represents the spatial relationship among salient landmarks of an environment, is developed to solve navigation problems. Inspired by the navigation ability of humans or animals, the studies, [4][5][6] which are related to how mammals perform mapping, localization and navigation, have gained extraordinary interests from the robotics community.…”