The Seamless Peer and Cloud Evolution Framework

Evolutionary algorithms are increasingly being applied to problems that are too computationally expensive to run on a single personal computer due to costly fitness function evaluations and/or large numbers of fitness evaluations. Here, we introduce the Seamless Peer And Cloud Evolution (SPACE) framework, which leverages bleeding edge web technologies to allow the computational resources necessary for running large scale evolutionary experiments to be made available to amateur and professional researchers alike, in a scalable and cost-effective manner, directly from their web browsers. The SPACE framework accomplishes this by distributing fitness evaluations across a heterogeneous pool of cloud compute nodes and peer computers. As a proof of concept, this framework has been attached to the RoboGen open-source platform for the co-evolution of robot bodies and brains, but importantly the framework has been built in a modular fashion such that it can be easily coupled with other evolutionary computation systems.

Automatic Gait Generation in Modular Robots: to Oscillate or to Rotate? that is the question

Auke Ijspeert, Soha Pouya, Alexander Spröwitz, Jesse van den Kieboom

Modular robots offer the possibility to design robots with a high diversity of shapes and functionalities. This nice feature also brings an important challenge: namely how to design efficient locomotion gaits for arbitrary robot structures with many degrees of freedom. In this paper, we present a framework that allows one to explore and identify highly different gaits for a given arbitraryshaped modular robot. For this, we use simulated robots made of several Roombots modules that have three rotational joints each. These modules have as interesting feature that they can produce both oscillatory movements (i.e. periodic movements around a rest position) and rotational movements (i.e. with continuously increasing angle), leading to very rich locomotion patterns. Here we ask ourselves which types of movements — purely oscillatory, purely rotational, or a combination of both— lead to the fastest gaits. To address this question we designed a control architecture based on a distributed system of coupled phase oscillators that can produce synchronized rotations and oscillations in many degrees of freedom. We also designed a specific optimization algorithm that can automatically design hybrid controllers, i.e. controllers that use oscillations in some joints and rotations in others, for fast gaits. The proposed framework is verified through multiple simulations for several robot morphologies. The results show that (i) the question whether it is better to oscillate or to rotate depends on the morphology of the robot, and that in general it is best to do both, (ii) the optimization framework can successfully generate hybrid controllers that outperform purely oscillatory and purely rotational ones, and (iii) the resulting gaits are fast, innovative, and would have been hard to design by hand

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Towards a theoretical foundation for morphological computation with compliant bodies

Auke Ijspeert, Rolf Pfeifer

The control of compliant robots is, due to their often nonlinear and complex dynamics, inherently difficult. The vision of morphological computation proposes to view these aspects not only as problems, but rather also as parts of the solution. Non-rigid body parts are not seen anymore as imperfect realizations of rigid body parts, but rather as potential computational resources. The applicability of this vision has already been demonstrated for a variety of complex robot control problems. Nevertheless, a theoretical basis for understanding the capabilities and limitations of morphological computation has been missing so far. We present a model for morphological computation with compliant bodies, where a precise mathematical characterization of the potential computational contribution of a complex physical body is feasible. The theory suggests that complexity and nonlinearity, typically unwanted properties of robots, are desired features in order to provide computational power. We demonstrate that simple generic models of physical bodies, based on mass-spring systems, can be used to implement complex nonlinear operators. By adding a simple readout (which is static and linear) to the morphology, such devices are able to emulate complex mappings of input to output streams in continuous time. Hence, by outsourcing parts of the computation to the physical body, the difficult problem of learning to control a complex body, could be reduced to a simple and perspicuous learning task, which can not get stuck in local minima of an error function.

2011

Evolving Neural Controllers for Collective Robotic Inspection

Alcherio Martinoli

In this paper, an automatic synthesis methodology based on evolutionary computation is applied to evolve neural controllers for a homogeneous team of miniature autonomous mobile robots. Both feed-forward and recurrent neural networks can be evolved with fixed or variable network topologies. The efficacy of the evolutionary methodology is demonstrated in the framework of a realistic case study on collective robotic inspection of regular structures, where the robots are only equipped with limited local on-board sensing and actuating capabilities. The neural controller solutions generated during evolutions are evaluated in a sensorbased embodied simulation environment with realistic noise. It is shown that the evolutionary algorithms are able to successfully synthesize a variety of novel neural controllers that could achieve performances comparable to a carefully hand-tuned, rule-based controller in terms of both average performance and robustness to noise.

2006

Automatic Gait Generation in Modular Robots: to Oscillate or to Rotate? that is the question

Auke Ijspeert, Soha Pouya, Alexander Spröwitz, Jesse van den Kieboom

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010