A Biologically Inspired Biped Locomotion Strategy for Humanoid Robots: Modulation of Sinusoidal Patterns by a Coupled Oscillator Model

Morimoto, Jun; Endo, Gen; Nakanishi, Jun; Cheng, Gordon

doi:10.1109/tro.2008.915457

Cited by 109 publications

(75 citation statements)

References 21 publications

(29 reference statements)

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“…Our humanoid robot maintained its own balance when making a fast squat movement with a frequency up to 1. and modulated through the policy-learning process. In this review, as a target task, we generate biped walking movement by humanoid robots with a central pattern generator (CPG) model [87] that is used as the bottom-layer controller to generate the low-dimensional internal dynamics. Figure 8 shows our idea of a hierarchical learning framework.…”

Section: Hierarchical Architecture For Humanoid Motor Learningmentioning

confidence: 99%

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Morimoto¹,

Kawato

2015

J. R. Soc. Interface.

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In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the 'understanding the brain by creating the brain' approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain-machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.

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Section: Hierarchical Architecture For Humanoid Motor Learningmentioning

confidence: 99%

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Morimoto¹,

Kawato

2015

J. R. Soc. Interface.

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“…Although the robotics community eagerly embraced the concept of CPG models for swimming or walking robots [50,[53][54][55], this work reports the first CPG-based control for flapping flight. The use of nonlinear oscillators for insect flapping flight has also been suggested by some biologists [37,38].…”

Section: Fundamentals Of Neurobiologically Inspired Controlmentioning

confidence: 99%

Neurobiologically Inspired Control of Engineered Flapping Flight

Chung

Stoner

Dorothy

2009

AIAA Infotech@Aerospace Conference

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This paper presents a new control approach and a dynamic model for engineered flapping flight with many interacting degrees of freedom. This paper explores the applications of neurobiologically inspired control systems in the form of central pattern generators to control flapping-flight dynamics. A rigorous mathematical and control theoretic framework to design complex three-dimensional wing motions is presented based on phase synchronization of nonlinear oscillators. In particular, we show that flapping-flying dynamics without a tail or traditional aerodynamic control surfaces can be effectively controlled by a reduced set of central pattern generator parameters that generate phase-synchronized or symmetry-breaking oscillatory motions of two main wings. Furthermore, by using Hopf bifurcation, we show that tailless aircraft alternating between flapping and gliding can be effectively stabilized by smooth wing motions driven by the central pattern generator network. Results of numerical simulation with a full six-degree-of-freedom flight dynamic model validate the effectiveness of the proposed neurobiologically inspired control approach.

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“…3). We use a simplified COP detection method introduced in Morimoto et al (2006Morimoto et al ( , 2008.…”

Section: Phase Detectionmentioning

confidence: 99%

Nonparametric representation of an approximated Poincaré map for learning biped locomotion

Morimoto

Atkeson

2009

Auton Robot

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We propose approximating a Poincaré map of biped walking dynamics using Gaussian processes. We locally optimize parameters of a given biped walking controller based on the approximated Poincaré map. By using Gaussian processes, we can estimate a probability distribution of a target nonlinear function with a given covariance. Thus, an optimization method can take the uncertainty of approximated maps into account throughout the learning process. We use a reinforcement learning (RL) method as the optimization method. Although RL is a useful non-linear optimizer, it is usually difficult to apply RL to real robotic systems due to the large number of iterations required to acquire suitable policies. In this study, we first approximated the Poincaré map by using data from a real robot, and then applied RL using the estimated map in order to optimize stepping and walking policies. We show that we can improve stepping and walking policies both in simulated and real environments. Experimental validation on a humanoid robot of the approach is presented.

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A Biologically Inspired Biped Locomotion Strategy for Humanoid Robots: Modulation of Sinusoidal Patterns by a Coupled Oscillator Model

Cited by 109 publications

References 21 publications

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Neurobiologically Inspired Control of Engineered Flapping Flight

Nonparametric representation of an approximated Poincaré map for learning biped locomotion

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