Central pattern generators for locomotion control in animals and robots: A review

By Auke Jan Ijspeert. 2008

1,993 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cell Biology, Cognitive Neuroscience

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Citations in the corpus

Synchronization in complex networks of phase oscillators: A survey (2014, methodology): “…2008; Baldoni et al., 2010; Wang et al., 2013), central pattern generators for robotic locomotion (Aoi and Tsuchiya, 2005; Righetti and Ijspeert, 2006; Ijspeert, 2008), decentralized maximum likelihood estimation (Barbarossa and Scutari, 2007), and human-robot interaction (Mizumoto et al., 2010).”
Model Identification via Physics Engines for Improved Policy Search (2017, background): “Policy search can also be achieved through Bayesian optimization and has been used for gait optimization [14], where Central Pattern Generators are a popular policy parameterization [15].”
Learning, planning, and control for quadruped locomotion over challenging terrain (2011, background): “In the realm of biologically inspired legged locomotion, there has also been research on biologically inspired controllers, usually in the form of central pattern generators (CPGs) (Ijspeert 2008).”
A Muscle-Reflex Model That Encodes Principles of Legged Mechanics Produces Human Walking Dynamics and Muscle Activities (2010, background): “While it is generally accepted that CPGs can form a central drive for motor activity [4], [6], [ 54 ], their functional role in human locomotion is debated [43], [55].”
Interactive Control of Diverse Complex Characters with Neural Networks (2015, methodology): “For flying and swimming yet another set of control architectures, commonly making use of explicit cyclic encodings, have been used [8, 7].”
Combining the benefits of function approximation and trajectory optimization (2014, background): “Some of the most interesting robotic behaviors generated by neural networks are swimming and crawling [9] but they do not involve control hazards such as falling, dropping an object or hitting an obstacle.”
Self-organized adaptation of a simple neural circuit enables complex robot behaviour (2010, background): “Nature apparently has succeeded in creating circuitries specific for such purpose”
A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning (2012, methodology): “Study of these models is beyond the scope of this thesis, and we refer interested readers to (Ijspeert, 2008) for a survey on the CPG models.”
Reservoir Computing Trends (2012, background): “Modulatable neural pattern generation is an extensive field of research [16].”
Single-unit pattern generators for quadruped locomotion (2013, background): “Evolution and analysis of model CPGs for walking: I. dynamical modules.”
Controlling Recurrent Neural Networks by Conceptors (2014, background): “Frequency adaptation of neural oscillators is an important theme in research on biological pattern generators (CPGs) (reviews: [40, 50]).”
Gait optimization for roombots modular robots — Matching simulation and reality (2013, background): “We control Roombots structures through central pattern generators (CPG) [19] - networks of coupled nonlinear oscillators - for the following reasons: (i) CPGs are capable”