A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-like Network

Ismael Baira Ojeda, Silvia Tolu, Henrik Hautop Lund

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Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit. The LWPR guarantees both an optimized representation of the input space and the learning of the dynamic internal model (IM) of the robot. However, the cerebellar-like sub-circuit integrates LWPR input-driven contributions to deliver accurate corrective commands to the global IM. This article extends the earlier work by including the Deep Cerebellar Nuclei (DCN) and by reproducing the Purkinje and the DCN layers using a spiking neural network (SNN) implemented on the neuromorphic SpiNNaker platform. The performance and robustness outcomes from the real robot tests are promising for neural control scalability.
Original languageEnglish
Title of host publicationProceedings of Living Machines 2017
Publication date2017
Publication statusPublished - 2017
EventLiving Machines 2017 - Stanford University, Stanford, United States
Duration: 25 Jul 201728 Jul 2017


ConferenceLiving Machines 2017
LocationStanford University
CountryUnited States
SeriesLecture Notes in Computer Science


  • Neuro-robotics
  • Bio-inspiration
  • Motor control
  • Cerebellum
  • Machine Learning
  • Compliant control
  • Internal model


HBP SGA1: Human Brain Project. Subproject 10 Neurorobotics Platform - SGA1

Tolu, S., Lund, H. H., Capolei, M. C., Corchado Miralles, C. & Baira Ojeda, I.


Project: Research

Cite this

Baira Ojeda, I., Tolu, S., & Lund, H. H. (2017). A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-like Network. In Proceedings of Living Machines 2017 (pp. 375-386). Springer. Lecture Notes in Computer Science, Vol.. 10384 https://doi.org/10.1007/978-3-319-63537-8 31