Fable II: Design of a modular robot for creative learning

Pacheco, Moises; Fogh, Rune; Lund, Henrik Hautop; Christensen, David Johan

doi:10.1109/icra.2015.7140060

Cited by 19 publications

(17 citation statements)

References 19 publications

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“…The forward model and the cerebellar-like microcircuit were reproduced by combining machine learning and computational neuroscience techniques. The control scheme was then tested on a real physical platform, the Fable modular robot, 14 combining basic modules that led to a mutual influence in the joint dynamics. In this context, a real modular robot represents a suitable benchmark platform to investigate the role of the cerebellum in anticipatory control and the relation with its internal modularity.…”

Section: Introductionmentioning

confidence: 99%

A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control

Tolu

Capolei

Vannucci

et al. 2019

Int. J. Neur. Syst.

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The cerebellum, which is responsible for motor control and learning, has been suggested to act as a Smith predictor for compensation of time-delays by means of internal forward models. However, insights about how forward model predictions are integrated in the Smith predictor have not yet been unveiled. To fill this gap, a novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed. The goal is to provide accurate anticipatory corrections to the generation of the motor commands in spite of sensory delays and to validate the robustness of the proposed control method to input and physical dynamic changes. The outcome of the proposed architecture with other two control schemes that do not include the Smith control strategy or the cerebellar-like corrections are compared. The results obtained on four sets of experiments confirm that the cerebellum-like circuit provides more effective corrections when only the Smith strategy is adopted and that minor tuning in the parameters, fast adaptation and reproducible configuration are enabled.

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Section: Introductionmentioning

confidence: 99%

A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control

Tolu

Capolei

Vannucci

et al. 2019

Int. J. Neur. Syst.

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“…Fable is a modular robot system consisting of detachable modules [32]. This project uses its joint module SDF model to run the robot simulations in NRP.…”

Section: E Fable Robotmentioning

confidence: 99%

Integration of paired spiking cerebellar models for voluntary movement adaptation in a closed-loop neuro-robotic experiment. A simulation study

Corchado¹,

Antonietti

Capolei

et al. 2019

2019 IEEE International Conference on Cyborg and Bionic Systems (CBS)

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Motor control is a very important feature in the human brain to achieve optimal performance in motor tasks. The biological basis of this feature can be better understood by emulating the cerebellar mechanisms of learning. The cerebellum plays a key role in implementing fine motor control, since it extracts the information about movements from sensorymotor signals, stores it by means of internal models and uses them to adapt to the environment. The hypothesis is that different internal models could work both independently and dependently. So far, there have been a few studies that aimed to prove their dependency; however, this hypothesis has not been widely used in robot control. The purpose of this work is to build paired spiking cerebellar models and to incorporate them into a biologically plausible composite robotic control architecture for movement adaptation. This is achieved by combining feedback error learning and cerebellar internal models theories. Thus the control architecture is composed of cerebellar feed-forward and recurrent loops for torque-based control of a robot. The spiking cerebellar models are able to correct and improve the performance of the two-degrees of freedom robot module Fable by providing both adaptive torque corrections and sensory corrections to the reference generated by the trajectory planner. Simulations are carried out in the Neurorobotics platform of the Human Brain Project. Results show that the contribution provided by cerebellar learning leads to an optimization of the performance with errors being reduced by 30% compared with the case where the cerebellar contribution is not applied.

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“…Fable II modular robot 40 presents a genderless docking system that is 90 symmetric. Two different (small and medium) size connectors, which are cross-compatible, are used in this system.…”

Section: Magneticmentioning

confidence: 99%

“…Most of the recent systems feature magnetic-and electromechanical-based docking systems. Magnetic-based docking systems are common among manually configurable systems like Fable II 40 and M…”

Section: Docking Trendsmentioning

confidence: 99%

Current trends in reconfigurable modular robots design

Brunete

Ranganath

Segovia

et al. 2017

International Journal of Advanced Robotic Systems

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This article presents a review on trends in modular reconfigurable robots, comparing the evolution of the features of the most significant robots over the years and focusing on the latest designs. These features are reconfiguration, docking, degrees of freedom, locomotion, control, communications, size, and powering. For each feature, some of the most relevant designs are presented and the current trends in the design are discussed.

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Fable II: Design of a modular robot for creative learning

Cited by 19 publications

References 19 publications

A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control

A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control

Integration of paired spiking cerebellar models for voluntary movement adaptation in a closed-loop neuro-robotic experiment. A simulation study

Current trends in reconfigurable modular robots design

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