We propose the IDeM-MRS learning formalism to be used by a group of robots for solving practical tasks in indoor environments. The formalism is inspired on the theory of social learning models for human beings that is traditionally developed in Psychology and Education fields. Our model can be used for coordination of the group, as for, allowing assimilation and accommodation of knowledge through experience exchange. Besides explaining the theoretical model itself, we formalize the mathematics involved with it in a very simple and straightforward fashion. Some issues are especially investigated such as the realistic representation of the multi-robot environment involving the global mission, the tasks belonging to the mission and the active set of robots. A way for task selection is proposed based on social learning theories and approaches that allow cooperative and efficient execution of tasks by robots. To this end, IDeM-MRS can be used in different types of missions varying from simple to complex. Experiments and results validate the efficiency of the formalism compared to a traditional empirical model.

SummaryWe propose the N-learning practical approach for teaching and learning behaviors in a multirobot system, which is performed through mandatory behavior acquisition based on interactions between the robots at execution time. The proposed methodology can be used to self-program the robots of a team by programming only a single robot with a set of codes that contain behaviors to be transferred and used by other robots as necessary. These codes are implemented in a modular fashion. An advantage of our approach is that when a team of robots is required to perform a specific mission, the set of behaviors required to accomplish that mission can be implemented only once in a single robot or in a distributed fashion. Then, these distributed behaviors are transferred to each of the other robots in the team according to their demand, without the need to reprogram them by hand since the robots in the team can share them autonomously. As an application example, a human critic can teach (or program) only one or a few robots, and these robots are thus able to exchange knowledge with the other team members since they have been preinstalled to run the N-learning system basics. Simulated and real robot experiments are performed to demonstrate the feasibility and validation of our approach.

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