Central Pattern Generators with phase regulation for the control of humanoid locomotion

Matos, Vitor; Santos, Cristina P.

doi:10.1109/humanoids.2012.6651510

Cited by 14 publications

(27 citation statements)

References 12 publications

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“…At this point both legs support the robot simultaneously, describing a double support phase, transiting from one single support phase to the contralateral single support phase. A detailed description as well as an extensive analysis of this CPG solution is provided in [14].…”

Section: Bipedal Cpg Approachmentioning

confidence: 99%

“…The motion primitives used in this work, common to both legs, consist of a set of coarse sinusoidal and bell-shaped motions pieced together from observations and kinematic descriptions observable from human walking [14], and constitute a basic motor repertoire that achieves a basic but capable walking behavior. Sinusoidal and bell-shaped motions are described by:…”

Section: Bipedal Cpg Approachmentioning

confidence: 99%

“…Walking motor patterns are considered to be organized as coupled unit-burst elements. Each unit generator is responsible for the activation of a single joint motion and composed by a motion pattern generator driven by a rhythmic generator [14,27] -a phase oscillator acting as the pace keeping clock in the generation of rhythmic motions in a given leg i, by setting where in the gait cycle the robot is currently in:…”

Section: Bipedal Cpg Approachmentioning

confidence: 99%

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Skill Memory in Biped Locomotion

André

Santos

Costa

2015

J Intell Robot Syst

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Robots must be able to adapt their motor behavior to unexpected situations in order to safely move among humans. A necessary step is to be able to predict failures, which result in behavior abnormalities and may cause irrecoverable damage to the robot and its surroundings, i.e. humans. In this paper we build a predictive model of sensor traces that enables early failure detection by means of a skill memory. Specifically, we propose an architecture based on a biped locomotion solution with improved robustness due to sensory feedback, and extend the concept of Associative Skill Memories (ASM) to periodic movements by introducing several mechanisms into the training workflow, such as linear interpolation and regression into a Dynamical Motion Primitive (DMP) system such that representation becomes time invariant and easily parameterizable. The failure detection mechanism applies statistical tests to determine the optimal operating conditions. Both training and failure testing were conducted on a DARwIn-OP inside a simulation environment to assess and validate the failure detection system proposed. Results show that the system performance in terms of the compromise between sensitivity and specificity is similar with and without the proposed mechanism, while achieving a significant data size reduction due to the periodic approach taken.

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Section: Bipedal Cpg Approachmentioning

confidence: 99%

Section: Bipedal Cpg Approachmentioning

confidence: 99%

Section: Bipedal Cpg Approachmentioning

confidence: 99%

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Skill Memory in Biped Locomotion

André

Santos

Costa

2015

J Intell Robot Syst

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“…Here we summarize all the motions assigned to each joint: (15) Further details about this approach can be found in [17]. A correct tuning of parameters, p, is necessary for achieving bipedal walking in the robot.…”

Section: Cpg Based Control Architecturementioning

confidence: 99%

“…The locomotion controller used in this work relies on a Central Pattern Generator (CPG) capable of generating bipedal locomotor behaviors, such as walking forward/backwards and turning, as described in [17]. The system could easily integrate local sensory feedback.…”

Section: Cpg Based Control Architecturementioning

confidence: 99%

Optimization of humanoid walking controller: Crossing the reality gap

Oliveira

Doncieux

Mouret

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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Abstract-Humanoid locomotion remains a challenge because of the inherent instability of such robotic platforms. Inspired from observations on animals, Central Pattern Generators have been proposed to support the generation of rhythmic patterns able to make a robot smoothly walk while requiring few computational power. Nevertheless, tuning such controllers is challenging, in particular because small irregularities in the walking pattern easily make the robot fall. Optimization algorithms can be used to tune them in simulation, but the transfer of such solutions to the real robot raises the reality gap problem, as a solution efficient in simulation may well be inefficient in reality. It is proposed here to use the transferability approach to solve this problem. Its principle is to learn a model of the transferability between simulation and reality while doing several evaluations on the real robot. This model is then used to estimate how well a controller will transfer onto the real robot and the optimization process tries to optimize it besides other cost functions related to locomotion and tested in simulation only. The approach has been applied to the DARWIN-OP robot.

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