Developing an embodied gait on a compliant quadrupedal robot

Degrave, Jonas; Caluwaerts, Ken; Dambre, Joni; wyffels, Francis

doi:10.1109/iros.2015.7354014

Cited by 22 publications

(19 citation statements)

References 21 publications

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“…This output could then be used as a command sequence sent to actuators. However, this approach was further complicated in a quadrupedal robot [19]. Nakajima et al [57] have used a model of a soft robotic arm inspired by the octopus.…”

Section: Physical Reservoir Computingmentioning

confidence: 99%

“…This very much blurs the boundaries between the brain as the seat of computing and the "mere physical body," in accordance with biological reality, which will be discussed in the next section. However, thus far the examples of this type [28,57,58,11,19,37] have a theoretical or proof-of-concept character, and their applicability remains to be proven ( Figure 9I-J).…”

Section: Morphological Computationmentioning

confidence: 99%

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What Is Morphological Computation? On How the Body Contributes to Cognition and Control

2017

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The contribution of the body to cognition and control in natural and artificial agents is increasingly described as "offloading computation from the brain to the body," where the body is said to perform "morphological computation." Our investigation of four characteristic cases of morphological computation in animals and robots shows that the "offloading" perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that facilitates control, (2) morphology that facilitates perception, and the rare cases of (3) morphological computation proper, such as reservoir computing, where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control-how it contributes to the overall orchestration of intelligent behavior.

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Section: Physical Reservoir Computingmentioning

confidence: 99%

Section: Morphological Computationmentioning

confidence: 99%

What Is Morphological Computation? On How the Body Contributes to Cognition and Control

2017

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“…By combining the body feedback with some additional computational power (e.g. a 'brain'), more complex locomotion tasks can be accomplished (Degrave et al, 2015). The computations that naturally occur in the body are then augmented with a small 'brain' to achieve partially embodied control.…”

Section: Introductionmentioning

confidence: 99%

“…The computations that naturally occur in the body are then augmented with a small 'brain' to achieve partially embodied control. In Degrave et al (2015) this was found to be necessary for gait generation with a quadruped robot. In Burms et al (2015) and Urbain et al (2017), more complex tasks were addressed using, respectively, a tensegrity robot and a mass-spring network.…”

Section: Introductionmentioning

confidence: 99%

Populations of spiking neurons for reservoir computing: Closed loop control of a compliant quadruped

Vandesompele

Urbain

wyffels

et al. 2019

Cognitive Systems Research

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Compliant robots can be more versatile than traditional robots, but their control is more complex. The dynamics of compliant bodies can however be turned into an advantage using the physical reservoir computing framework. By feeding sensor signals to the reservoir and extracting motor signals from the reservoir, closed loop robot control is possible. Here, we present a novel framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.

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“…They show a walking pattern that looks natural and energy efficient compared to traditional stiff controlled robots. In other fields of robotics, we can also cite the works of Iida and Pfeifer (2006) or Degrave et al (2015), in which dynamical properties of compliant quadruped robots are used to provide low power consumption, to reduce controller computational complexity, and to observe natural transitions between gaits. Examples that clearly benefit from compliance to improve moving can also be found, among others, in Cham et al (2004) which focuses on hexapod locomotion.…”

Section: Introductionmentioning

confidence: 99%

Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning

et al. 2017

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Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size.

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Developing an embodied gait on a compliant quadrupedal robot

Cited by 22 publications

References 21 publications

What Is Morphological Computation? On How the Body Contributes to Cognition and Control

What Is Morphological Computation? On How the Body Contributes to Cognition and Control

Populations of spiking neurons for reservoir computing: Closed loop control of a compliant quadruped

Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning

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