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.
Abstract-We present a generic algorithm for self calibration of robotic systems that utilizes two key innovations. First, it uses information theoretic measures to automatically identify and store novel measurement sequences. This keeps the computation tractable by discarding redundant information and allows the system to build a sparse but complete calibration dataset from data collected at different times. Second, as the full observability of the calibration parameters may not be guaranteed for an arbitrary measurement sequence, the algorithm detects and locks unobservable directions in parameter space using a truncated QR decomposition of the Gauss-Newton system. The result is an algorithm that listens to an incoming sensor stream, builds a minimal set of data for estimating the calibration parameters, and updates parameters as they become observable, leaving the others locked at their initial guess.Through an extensive set of simulated and real-world experiments, we demonstrate that our method outperforms state-of-the-art algorithms in terms of stability, accuracy, and computational efficiency.
We present a generic algorithm for self-calibration of robotic systems that utilizes two key innovations. First, it uses an information-theoretic measure to automatically identify and store novel measurement sequences. This keeps the computation tractable by discarding redundant information and allows the system to build a sparse but complete calibration dataset from data collected at different times. Second, as the full observability of the calibration parameters may not be guaranteed for an arbitrary measurement sequence, the algorithm detects and locks unobservable directions in parameter space using a combination of rank-revealing QR and singular value decompositions of the Fisher information matrix. The result is an algorithm that listens to an incoming sensor stream, builds a minimal set of data for estimating the calibration parameters, and updates parameters as they become observable, leaving the others locked at their initial guess. We validate our approach through an extensive set of simulated and real-world experiments.
We present a Bluetooth scatternet protocol (SNP) that provides the user with a serial link to all connected members in a transparent wireless Bluetooth network. By using only local decision making we can reduce the overhead of our scatternet protocol dramatically. We show how our SNP software layer simplifies a variety of tasks like the synchronization of central pattern generator controllers for actuators, collecting sensory data and building modular robot structures. The whole Bluetooth software stack including our new scatternet layer is implemented on a single Bluetooth and memory chip. To verify and characterize the SNP we provide data from experiments using real hardware instead of software simulation. This gives a realistic overview of the scatternet performance showing higher order effects that are difficult to be simulated correctly and guaranties the correct function of the SNP in real world applications.
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