Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology

Ijspeert, Auke Jan; Hallam, John; Willshaw, David

doi:10.1177/105971239900700202

Cited by 76 publications

(52 citation statements)

References 25 publications

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“…Like in the real lamprey, turning can be obtained by inducing higher amplitude oscillations on one side of the double chain (see below). Compared to previous neural network models that we developed of the lamprey CPG [25], [26], the model in this article is simpler (much fewer state variables) and therefore better suited for being programmed on a microcontroller on board of the robot, while keeping the essential features of lamprey travelling wave generation.…”

Section: A Central Pattern Generator Modelmentioning

confidence: 99%

Online trajectory generation in an amphibious snake robot using a lamprey-like central pattern generator model

Ijspeert

Crespi

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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117

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Abstract-This article presents a control architecture for controlling the locomotion of an amphibious snake/lamprey robot capable of swimming and serpentine locomotion. The control architecture is based on a central pattern generator (CPG) model inspired from the neural circuits controlling locomotion in the lamprey's spinal cord. The CPG model is implemented as a system of coupled nonlinear oscillators on board of the robot. The CPG generates coordinated travelling waves in real time while being interactively modulated by a human-operator. Interesting aspects of the CPG model include (1) that it exhibits limit cycle behavior (i.e. it produces stable rhythmic patterns that are robust against perturbations), (2) that the limit cycle behavior has a closed-form solution which provides explicit control over relevant characteristics such as frequency, amplitude and wavelength of the travelling waves, and (3) that the control parameters of the CPG can be continuously and interactively modulated by a human operator to offer high maneuverability. We demonstrate how the CPG allows one to easily adjust the speed and direction of locomotion both in water and on ground while ensuring that continuous and smooth setpoints are sent to the robot's actuated joints.

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Section: A Central Pattern Generator Modelmentioning

confidence: 99%

Online trajectory generation in an amphibious snake robot using a lamprey-like central pattern generator model

Ijspeert

Crespi

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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117

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“…Designing such potential fields for a given behavior is often a hard problem, with few analytically sound approaches (Koditschek, 1987). Due to this lack of analytical tractability, some people have suggested recurrent neural network (Paine & Tani, 2004;Jaeger & Haas, 2004) or evolutionary methods (Ijspeert, Hallam, & Willshaw, 1999;Ijspeert, 2001) as a design strategy for nonlinear dynamical systems controllers.…”

Section: Robotics and Control Theorymentioning

confidence: 99%

Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

et al. 2013

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Nonlinear dynamical systems have been used in many disciplines to model complex behaviors, including biological motor control, robotics, perception, economics, traffic prediction, and neuroscience. While often the unexpected emergent behavior of nonlinear systems is the focus of investigations, it is of equal importance to create goal-directed behavior (e.g., stable locomotion from a system of coupled oscillators under perceptual guidance). Modeling goal-directed behavior with nonlinear systems is, however, rather difficult due to the parameter sensitivity of these systems, their complex phase transitions in response to subtle parameter changes, and the difficulty of analyzing and predicting their long-term behavior; intuition and time-consuming parameter tuning play a major role. This letter presents and reviews dynamical movement primitives, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques. The essence of our approach is to start with a simple dynamical system, such as a set of linear differential equations, and transform those into a weakly nonlinear system with prescribed attractor dynamics by means of a learnable autonomous forcing term. Both point attractors and limit cycle attractors of almost arbitrary complexity can be generated. We explain the design principle of our approach and evaluate its properties in several example applications in motor control and robotics.

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“…EA and RL can optimize the parameters of the controller (e. g. a neural network) and can in principle achieve the behaviors demonstrated here. There are many impressive results where systems of similar dynamical complexity have been successfully controlled, see for example (EA) Chemova and Veloso (2004); Bongard et al (2006); Mazzapioda and Nolfi (2006);de Margerie et al (2007); Ijspeert et al (1999) and (RL) Peters and Schaal (2008). In high-dimensional systems, however, identical subcomponents are typically used or the problem is appropriately prestructured by hand.…”

Section: Discussionmentioning

confidence: 97%

Robot Learning by Guided Self-Organization

Martius

Der

Herrmann

2014

Guided Self-Organization: Inception

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Self-organizing processes are not only crucial for the development of living beings, but can also spur new developments in robotics, e. g. to increase fault tolerance and enhance flexibility, provided that the prescribed goals can be realized at the same time. This combination of an externally specified objective and autonomous exploratory behavior is very interesting for practical applications of robot learning. In this chapter, we will present several forms of guided self-organization in robots based on homeokinesis.Self-organization in the sense used in natural sciences means the spontaneous creation of patterns in space and/or time in systems consisting of many individual components. This involves the emergence, meaning the spontaneous creation, of structures or functions that are not directly explainable from the interactions between the constituents of the system. Examples are for instance spontaneous magnetization, convection patterns and reaction diffusion systems leading to the wonderful coloring of shells or animal coats. For robotic applications it is important to translate self-organization effects to a single robots considered as complex physical systems consisting of many constituents that are constraining each other in an intensive

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Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology

Cited by 76 publications

References 25 publications

Online trajectory generation in an amphibious snake robot using a lamprey-like central pattern generator model

Online trajectory generation in an amphibious snake robot using a lamprey-like central pattern generator model

Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

Robot Learning by Guided Self-Organization

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