Musculoskeletal Robots: Scalability in Neural Control

Richter, Christoph; Jentzsch, Sören; Hostettler, Rafael; Garrido, Jesús; Ros, Eduardo; Knoll, Alois; Röhrbein, Florian; Smagt, Patrick van der; Conradt, Jörg

doi:10.1109/mra.2016.2535081

Cited by 56 publications

(34 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other works, SpiNNaker was used to control a musculoskeletal robot in real-time [37] as well as decoding motor imagery movements from EEG signal [38]. Furthermore, SpiNNaker was used for audio samples [39] and handwritten digits classification [40].…”

Section: Spinnaker Neuromorphic Hardware Applicationsmentioning

confidence: 99%

Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware

Behrenbeck¹,

Tayeb²,

Bhiri³

et al. 2019

J. Neural Eng.

Self Cite

View full text Add to dashboard Cite

Objective. The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in timeseries signals and decode them without the use of hand-crafted features and vectorbased learning and the realization of the spiking model on low-power neuromorphic hardware. Approach. The NeuCube spiking model was used to classify different grasp movements directly from raw surface electromyography signals (sEMG), the estimations of the applied finger forces as well as the classification of two motor imagery movements from raw electroencephalography (EEG). In a parallel investigation, the designed spiking decoder was implemented on SpiNNaker neuromorphic hardware, which allows low-energy real-time processing. Results. Experimental results reveal a better classification accuracy using the NeuCube model compared to traditional machine learning methods. For sEMG classification, we reached a training accuracy of 85% and a test accuracy of 84.8%, as well as less than 19% of relative root mean square error (rRMSE) when estimating finger forces from six subjects. For the EEG classification, a mean accuracy of 75% was obtained when tested on raw EEG data from nine subjects from the existing 2b dataset from "BCI Competition IV". Significance. This work provides a proof of concept for a successful implementation of the NeuCube spiking model on the SpiNNaker neuromorphic platform for raw sEMG and EEG decoding, which could chart a route ahead for a new generation of portable closedloop and low-power neuroprostheses.

show abstract

Section: Spinnaker Neuromorphic Hardware Applicationsmentioning

confidence: 99%

Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware

Behrenbeck¹,

Tayeb²,

Bhiri³

et al. 2019

J. Neural Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Especially for Kenshiro, with his very large number of muscles, learning is a must. A very recent and important step in this direction is the work of (Richter et al 2016) who have combined a musculoskeletal robotics toolkit (Myorobotics) with a scalable neuromorphic computing platform (SpiNNaker) and demonstrated control of a musculoskeletal joint with a simulated cerebellum.…”

Section: Human-like Cognition In Robotsmentioning

confidence: 99%

Robots as powerful allies for the study of embodied cognition from the bottom up

Hoffmann¹,

Pfeifer²

2018

The Oxford Handbook of 4E Cognition

View full text Add to dashboard Cite

A large body of compelling evidence has been accumulated demonstrating that embodiment -the agent's physical setup, including its shape, materials, sensors and actuators -is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe.

show abstract

“…Most of the known applications rely on groups of robots or mobile agents, e.g. [20][21][22][23]. Of course, it is not always possible to provide groups of robot devices for experiments.…”

Section: How To Convert the 2d Into 3d Coordinates;mentioning

confidence: 99%

Robot Self-Detection System

Penev¹

2018

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

The paper presents design and implementation of a mobile robot, located in an accommodation. As opposed to other known solutions, the presented one is entirely based on standard, cheap and accessible devices and tools. An algorithm for transformation of the 2D coordinates of the robot into 3D coordinates is described. The design and implementation of the system are presented. Finally, experimental results with different devices are shown.

show abstract

Musculoskeletal Robots: Scalability in Neural Control

Cited by 56 publications

References 26 publications

Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware

Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware

Robots as powerful allies for the study of embodied cognition from the bottom up

Robot Self-Detection System

Contact Info

Product

Resources

About