This article addresses the problem of how modular robotics systems, i.e. systems composed of multiple modules that can be configured into different robotic structures, can learn to locomote. In particular, we tackle the problems of online learning, that is, learning while moving, and the problem of dealing with unknown arbitrary robotic structures.We propose a framework for learning locomotion controllers based on two components: a central pattern generator (CPG) and a gradient-free optimization algorithm: Powell's method. The CPG is implemented as a system of coupled nonlinear oscillators in our YaMoR modular robotic system, with one oscillator per module. The nonlinear oscillators are coupled together across modules using Bluetooth communication to obtain specific gaits, i.e. synchronized patterns of oscillations among modules. Online learning is done by running the Powell optimization algorithm in parallel to the CPG model, with the speed of locomotion being the criterion to be optimized. Interesting aspects of the optimization are: it is carried out online, it does not require stopping or resetting the robots, and it is fast.We present results showing the interesting properties of this framework for a modular robotic system. In particular, our CPG model can readily be implemented in a distributed system, it is cheap computationally, it exhibits limit cycle behavior (temporary perturbations are rapidly forgotten), it produces smooth trajectories even when control parameters are abruptly changed, and it is robust against imperfect communication among modules. We also present results of learning to move with three different robot structures. Interesting locomotion modes are obtained after running the optimization for less than 60 minutes.

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Modular robots offer a robust and flexible framework for exploring adaptive locomotion control. They allow assembling robots of different types e.g. snakelike robots, robots with limbs, and many other different shapes. In this paper we present a new cheap modular robot called YaMoR (for "Yet another Modular Robot"). Each YaMoR module contains an FPGA and a microcontroller supporting a wide range of control strategies and high computational power. The Bluetooth interface included in each YaMoR module allows wireless communication between the modules and controlling the robot from a PC. With the help of our control software called Bluemove, we tested different configurations of our YaMoR robots like a wheel, caterpillar or configurations with limbs and their capabilities for locomotion.

Purpose -This paper aims to present a novel modular robot that provides a flexible framework for exploring adaptive locomotion. Design/methodology/approach -A new modular robot is presented called YaMoR (for "Yet another Modular Robot"). Each YaMoR module contains an FPGA and a microcontroller supporting a wide range of control strategies and high computational power. The Bluetooth interface included in each YaMoR module allows wireless communication between the modules and controlling the robot from a PC. A control software called Bluemove was developed and implemented that allows easy testing of the capabilities for locomotion of a large variety of robot configurations. Findings -With the help of the control software called Bluemove, different configurations of the YaMoR modules were tested like a wheel, caterpillar or configurations with limbs and their capabilities for locomotion. Originality/value -This paper demonstrates that modular robots can act as a powerful framework for exploring locomotion of a large variety of different types of robots. Although present research is limited to exploring locomotion, YaMoR modules are designed to be general purpose and support a variety of applications.

This paper presents our work towards a decentralized reconfiguration strategy for self-reconfiguring modular robots, assembling furniture-like structures from Roombots (RB) metamodules. We explore how reconfiguration by locomotion from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using Roombots metamodules-two Roombots modules connected serially-that use broadcast signals, lookup tables of their movement space, assumptions about their neighborhood, and connections to a structured surface to collectively build desired structures without the need of a centralized planner.