Towards a general neural controller for quadrupedal locomotion

Maufroy, Christophe; Kimura, Hiroshi; Takase, Kunikatsu

doi:10.1016/j.neunet.2008.03.010

Cited by 45 publications

(49 citation statements)

References 32 publications

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“…There is currently no systematic methodology for designing a CPG controller; each individual CPG model has been designed on a completely ad hoc basis by focusing, in particular, on the interlimb neural connections in the CPG [11][12][13][14][15][16][17][18][19][20][21][22]. To establish a design methodology, we have reworked the design principle of CPG-based control [28,29].…”

Section: Resultsmentioning

confidence: 99%

Simple robot suggests physical interlimb communication is essential for quadruped walking

Owaki

Kano

Nagasawa

et al. 2013

J. R. Soc. Interface.

153

145

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Quadrupeds have versatile gait patterns, depending on the locomotion speed, environmental conditions and animal species. These locomotor patterns are generated via the coordination between limbs and are partly controlled by an intraspinal neural network called the central pattern generator (CPG). Although this forms the basis for current control paradigms of interlimb coordination, the mechanism responsible for interlimb coordination remains elusive. By using a minimalistic approach, we have developed a simple-structured quadruped robot, with the help of which we propose an unconventional CPG model that consists of four decoupled oscillators with only local force feedback in each leg. Our robot exhibits good adaptability to changes in weight distribution and walking speed simply by responding to local feedback, and it can mimic the walking patterns of actual quadrupeds. Our proposed CPG-based control method suggests that physical interaction between legs during movements is essential for interlimb coordination in quadruped walking.

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Section: Resultsmentioning

confidence: 99%

Simple robot suggests physical interlimb communication is essential for quadruped walking

Owaki

Kano

Nagasawa

et al. 2013

J. R. Soc. Interface.

153

145

View full text Add to dashboard Cite

show abstract

“…In particular, we think that our approach, combined with a high-level planning system, is particularly well-suited for locomotion, as the same system integrates the primitives needed for both rhythmic motion generation and posture control. Note that Kimura and his group obtained excellent results for locomotion based on CPGs and bio-inspired reflexes (Kimura et al (2007), Maufroy et al (2008)), but here our goal is to provide a more general approach to movement generation.…”

Section: Discussionmentioning

confidence: 99%

“…(2003), Gribovskaya and Billard (2008), Pastor et al (2009), Kober and Peters (2010)], rehabilitation (Ronsse et al (2010)), locomotion [e.g., Kimura et al (2007), Maufroy et al (2008)] and modular robotics [e.g., Cui et al (2010), Sproewitz et al (2010)] for instance. Here our focus is the generation of trajectories given simple, explicit high-level commands, as for instance in Maufroy et al (2008) for locomotion and Bullock and Grossberg (1988) and Hersch and Billard (2008) for reaching.…”

Section: Central Pattern Generatorsmentioning

confidence: 99%

“…Here our focus is the generation of trajectories given simple, explicit high-level commands, as for instance in Maufroy et al (2008) for locomotion and Bullock and Grossberg (1988) and Hersch and Billard (2008) for reaching. The novelty here is that we address the generation of both discrete and rhythmic movements through the same system, a subject that as received little attention so far, as we reviewed in Degallier and Ijspeert (2010).…”

Section: Central Pattern Generatorsmentioning

confidence: 99%

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Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

2011

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Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approach to movement generation: Movements results from combinations of a finite set of stable motor primitives organized at the spinal level. In this article we apply this concept of modular generation of movements to the control of robots with a high number of degrees of freedom, an issue that is challenging notably because planning complex, multidimensional trajectories in time-varying environments is a laborious and costly process. We thus propose to decrease the complexity of the planning phase through the use of a combination of discrete and rhythmic motor primitives, leading to the decoupling of the planning phase (i.e. the choice of behavior) and the actual trajectory generation. Such implementation eases the control of, and the switch between, different behaviors by reducing the dimensionality of the high-level commands. Moreover, since the motor primitives are generated by dynamical systems, the trajectories can be smoothly modulated, either by high-level commands to change the current behavior or by sensory feedback information to adapt to environmental constraints. In order to show the generality of our approach, we apply the framework to interactive drumming and infant crawling in a humanoid robot. These experiments illustrate the simplicity of the control architecture in terms of planning, the integration of different types * This work was supported by the European Commission's Cognition Unit, projects RobotCub and AMARSi. S.G. is funded by a IST-EPFL grant.of feedback (vision and contact) and the capacity of autonomously switching between different behaviors (crawling and simple reaching).

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“…An extension of this work, with an aim at biologically plausible control was presented in [8]. The controller was composed of different kinds of neurons, some responsible for the generation of the rhythmic joint trajectories for the legs and others responsible for shaping these trajectories using sensory information.…”

Section: Introductionmentioning

confidence: 99%

Learning robot gait stability using neural networks as sensory feedback function for Central Pattern Generators

Santos-Victor

Ijspeert

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper we present a framework to learn a model-free feedback controller for locomotion and balance control of a compliant quadruped robot walking on rough terrain. Having designed an open-loop gait encoded in a Central Pattern Generator (CPG), we use a neural network to represent sensory feedback inside the CPG dynamics. This neural network accepts sensory inputs from a gyroscope or a camera, and its weights are learned using Particle Swarm Optimization (unsupervised learning). We show with a simulated compliant quadruped robot that our controller can perform significantly better than the open-loop one on slopes and randomized height maps.

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Towards a general neural controller for quadrupedal locomotion

Cited by 45 publications

References 32 publications

Simple robot suggests physical interlimb communication is essential for quadruped walking

Simple robot suggests physical interlimb communication is essential for quadruped walking

Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

Learning robot gait stability using neural networks as sensory feedback function for Central Pattern Generators

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