FaNeuRobot: A Framework for Robot and Prosthetics Control Using the NeuCube Spiking Neural Network A @ICRA-cg8kk

Uploaded May 2018 | Updated September 2026, 2 weeks ago

ICRA 2018 Spotlight Video
Interactive Session Wed PM Pod I.6
Authors: Kumarasinghe, Kaushalya; Owen, Mahonri; Taylor, Denise; Kasabov, Nikola; Au, Chi Kit
Title: FaNeuRobot: A Framework for Robot and Prosthetics Control Using the NeuCube Spiking Neural Network Architecture & Finite Automata Theory

Abstract:
Limb amputation is a global problem. Prosthetic limbs can enhance the quality of life of amputees. To this end, anthropomorphic design and intuitive manipulation are two essential requirements. This paper presents a motor control framework for prosthetic control through Brain-Machine Interface (BMI) using Finite Automata Theory, and NeuCube Evolving Spiking Neural Network (SNN) architecture. Voluntary control of prosthetics requires decoding motor commands from the Central Nervous System of the amputee. Selection of the most suitable biomedical signal depends on many parameters such as level of amputation and muscle atrophy. Non-invasive BMI's have the possibility of supporting a wider range of amputees as it extracts the motor commands from the brain. In this paper, we present a proof of concept study on whether a cognitive computational model that is inspired by the motor control of the human body through muscle synergies combined with an anthropomorphic mechanical design, can result in accurate and robust prosthetic control through a non-invasive BMI. In future, learning of a complex Finite Automata that reflects complex upper limb motor behaviours will be investigated.

FaNeuRobot: A Framework for Robot and Prosthetics Control Using the NeuCube Spiking Neural Network A

Ultra-Wideband Radar for Robust Inspection Drone in Underground Coal Mines

Data Ferrying with Swarming UAS in Tactical Defence Networks

Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Bas

Performance Indicator for Benchmarking Force-Controlled Robots

A Tensegrity-Inspired Compliant 3-DOF Compliant Joint

$Enhancing Underwater Imagery Using Generative Adversarial Networks ICRA 2018 Spotlight Video Interactive Session Thu PM Pod J.7 Authors: Fabbri, Cameron; Islam, Md Jahidul; Sattar, Junaed Title: Enhancing Underwater Imagery Using Generative Adversarial Networks Abstract: Autonomous underwater vehicles (AUVs) rely on a variety of sensors acoustic, inertial and visual for intelligent decision making. Due to its non-intrusive, passive nature, and high information content, vision is an attractive sensing modality, particularly at shallower depths. However, factors such as light refraction and absorption, suspended particles in the water, and color distortion affect the quality of visual data, resulting in noisy and distorted images. AUVs that rely on visual sensing thus face difficult challenges, and consequently exhibit poor performance on vision-driven tasks. This paper proposes a method to improve the quality of visual underwater scenes using Generative Adversarial Networks (GANs), with the goal of improving input to vision-driven behaviors further down the autonomy pipeline. Furthermore, we show how recently proposed methods are able to generate a dataset for the purpose of such underwater image restoration. For any visually-guided underwater robots, this improvement can result in increased safety and reliability through robust visual perception. To that effect, we present quantitative and qualitative data which demonstrates that images corrected through the proposed approach generate more visually appealing images, and also provide increased accuracy for a diver tracking algorithm.$

Real-time Underwater 3D Reconstruction Using Global Context and Active Labeling

Landmark-Based Exploration with Swarm of Resource Constrained Robots

Straight-Leg Walking through Underconstrained Whole-Body Control

Dexterous Manipulation by Two Fingers with Coupled Joints

Integration of Local Geometry and Metric Information in Sampling-Based Motion Planning