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Controlling Collaborative Robots Using Robust Spiking Neural Networks

Project Description:

The way our brain works is still an open question, but one thing seems to be clear: biological neural systems are computationally powerful, robust and noisy. Natural nervous systems are able to control limbs in different scenarios with high precision. As neural networks in living beings communicate through spikes, modern neuromorphic systems try to mimic them by using spike-based neuron models. This prject is focused on the advancement of neurorobotics or brain inspired robotic arm controllers based on artificial neural network architectures. The architecture chosen to implement those controllers was the spike neuron version of Reservoir Computing framework, called Liquid State Machines. The main goal is to explore the possibility of using brain inspired neural networks to control a robot by demonstration. Moreover, it aims to achieve systems robust to environmental noise and internal structure destruction presenting a graceful degradation. As the validation, a series of action learning experiments are presented where robotic arms are controlled. The investigation starts with a 2 degrees of freedom arm and moves to the research version of the Rethink Robotics Inc. collaborative humanoid robot Baxter. The results show Liquid State Machines, when endowed with an extra external feedback loop, can be also employed to control more complex humanoid robotic arms than a simple planar 2 degrees of freedom one. Additionally, the new parallel architecture presented here was capable to withstand noise and internal destruction better than a simple use of multiple columns also presenting a graceful degradation behaviour.

My role in this project:

This project was the base of my PhD research from 2013 to 2017.


  1. Diverse, Noisy and Parallel: a New Spiking Neural Network Approach for Humanoid Robot Control
  2. Bee - The Spiking Reservoir (LSM) Simulator
  3. Graceful Degradation under Noise on Brain Inspired Robot Controllers
  4. Short-Term Plasticity in a Liquid State Machine Biomimetic Robot Arm Controller
  5. Sensor Fusion Approach Using Liquid StateMachines for Positioning Control


  1. Controlling the BAXTER robot using Liquid State Machines
  2. Animation of what happens inside a LSM