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A Biomimetic Learning Control Scheme for control of Modular Robots

Total Cost €


EC-Contrib. €






Project "BIOMODULAR" data sheet

The following table provides information about the project.


Organization address
postcode: 2800

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Denmark [DK]
 Project website
 Total cost 212˙194 €
 EC max contribution 212˙194 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-02-01   to  2019-01-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Motor control is a very important feature in the human brain for the performance of a motor skill. 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 from sensory-motor signals and uses it to respond to the environment. The purpose of this project is to benefit from the interplay between a body agent and an embodied artificial brain to understand the role of the first in the behavior of the latter and vice versa. The project aims to build a novel bio-inspired computational learning model for modular robots, and to incorporate it into a biologically plausible control scheme. The aforementioned model will merge machine learning techniques and a spiking modular cerebellum to develop a process that leads to the formation of long-term motor memories. Novel modular robots, such as Fable, will benefit from this adaptive predictive control system to perform desired, task-fulfilling behaviors. Exploiting this approach, the project pursues the discovery of important insights into the modular structure of the cerebellum, and its involvement in processing the sensory input for motor control tasks. The project will be developed at DTU with a run time of two years and will benefit from collaborations with other research groups (UGR and TUM). Their long expertise in neuromorphic computing and spiking networks will ensure that the candidate receives scientific training related to these fields (e.g. about cerebellar topology and cellular properties, and implementation of spiking networks in hardware). By providing multiple relevant contributions across the spectrum of the H2020 objectives in terms of its potential to advance robotic manufacturing, brain processing understanding, and novel computing paradigms, this project will enable the candidate to enhance her position at the forefront of advances in this fields.


year authors and title journal last update
List of publications.
2019 Silvia Tolu, Marie Claire Capolei, Lorenzo Vannucci, Cecilia Laschi, Egidio Falotico, Mauricio Vanegas Hernandez
A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control
published pages: , ISSN: 0129-0657, DOI: 10.1142/s012906571950028x
International Journal of Neural Systems 2019-10-14
2019 Corchado, C., Antonietti, A., Capolei, M. C., Casellato, C., & Tolu, S.
Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study.
published pages: , ISSN: , DOI:
In Proceedings of 2019 IEEE International Conference on Cyborg and Bionic Systems 2019-10-14
2019 Marie Claire Capolei, Nils Axel Andersen, Henrik Hautop Lund, Egidio Falotico, Silvia Tolu
A Cerebellar Internal Models Control Architecture for Online Sensorimotor Adaptation of a Humanoid Robot Acting in a Dynamic Environment
published pages: 1-1, ISSN: 2377-3766, DOI: 10.1109/lra.2019.2943818
IEEE Robotics and Automation Letters 2019-10-14
2019 Elisa Massi, Lorenzo Vannucci, Ugo Albanese, Marie Claire Capolei, Alexander Vandesompele, Gabriel Urbain, Angelo Maria Sabatini, Joni Dambre, Cecilia Laschi, Silvia Tolu, Egidio Falotico
Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion
published pages: , ISSN: 1662-5218, DOI: 10.3389/fnbot.2019.00071
Frontiers in Neurorobotics 13 2019-09-09
2019 Marie Claire Capolei, Emmanouil Angelidis, Egidio Falotico, Henrik Hautop Lund, Silvia Tolu
A Biomimetic Control Method Increases the Adaptability of a Humanoid Robot Acting in a Dynamic Environment
published pages: , ISSN: 1662-5218, DOI: 10.3389/fnbot.2019.00070
Frontiers in Neurorobotics 13 2019-09-09
2018 Capolei, Marie Claire; Falotico, Egidio; Lund, Henrik Hautop ; Tolu, Silvia
Distributed and Modular Bio-Inspired Architecture for Adaptive Motor Learning andControl
published pages: 92-97, ISSN: , DOI:
The Neural Bases of Action: from cellular microcircuits to large-scale networks and modelling 2019-09-09
2017 Ismael Baira Ojeda, Silvia Tolu, Henrik H. Lund
A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-like Network
published pages: 375-386, ISSN: 0302-9743, DOI: 10.1007/978-3-319-63537-8_31
Lecture Notes in Computer Science 2019-07-25

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