@article{, author = {Amaya, Camilo and von Arnim, Axel}, title = {Neurorobotic reinforcement learning for domains with parametrical uncertainty}, publisher = {Frontiers}, journal = {Frontiers in Neurorobotics}, volume = {17}, year = {2023}, month = oct, timestamp = 2023.10.25, location = {Lausanne, Switzerland}, abstract = {Neuromorphic hardware paired with brain-inspired learning strategies have enormous potential for robot control. Explicitly, these advantages include low energy consumption, low latency, and adaptability. Therefore, developing and improving learning strategies, algorithms, and neuromorphic hardware integration in simulation is a key to moving the state-of-the-art forward. In this study, we used the neurorobotics platform (NRP) simulation framework to implement spiking reinforcement learning control for a robotic arm. We implemented a force-torque feedback-based classic object insertion task (“peg-in-hole”) and controlled the robot for the first time with neuromorphic hardware in the loop. We therefore provide a solution for training the system in uncertain environmental domains by using randomized simulation parameters. This leads to policies that are robust to real-world parameter variations in the target domain, filling the sim-to-real gap.To the best of our knowledge, it is the first neuromorphic implementation of the peg-in-hole task in simulation with the neuromorphic Loihi chip in the loop, and with scripted accelerated interactive training in the Neurorobotics Platform, including randomized domains.}, howpublished = {article}, type = {article}, issn = {1662-5218}, doi = {10.3389/fnbot.2023.1239581}, keywords = {domain randomization, neuromorphic computing, neurorobotics, reinforcement learning, robot control, spiking neural networks}, url = {https://doi.org/10.3389/fnbot.2023.1239581}, } @inproceedings{, author = {Amaya, Camilo and Palinauskas, Gintautas and Eames, Evan and Neumeier, Michael and von Arnim, Axel}, title = {Generating Event-Based Datasets for Robotic Applications Using MuJoCo-ESIM}, booktitle = {Proceedings of the 2023 International Conference on Neuromorphic Systems}, publisher = {Association for Computing Machinery}, journal = {Proceedings of the 2023 International Conference on Neuromorphic Systems}, series = {ICONS 23}, volume = {1}, number = {11}, pages = {7}, year = {2023}, month = aug, timestamp = 2023.08.28, organization = {Association for Computing Machinery}, institution = {Association for Computing Machinery}, address = {New York, NY, USA}, location = {Santa Fe, NM, USA}, abstract = {Event-based cameras are cameras with high dynamic range that measure changes in light intensity at each pixel instead of capturing frames like traditional cameras. There are several event-based camera simulators for generating event-based datasets, each specialized towards a particular domain. However, none are designed specifically for robotic use-cases. This work addresses this issue using MuJoCo, a high performance physics engine; in combination with ESIM, an established event-generation method. To the authors' knowledge, this is the first robotic simulator tool for generating event-based datasets specifically designed for the robotics domain. Furthermore, to demonstrate its capabilities we generate an event-based visual dataset of industrial sockets, which is then used to train a SNN classifier.}, howpublished = {Proceedings}, isbn = {979-8-4007-0175-7}, doi = {10.1145/3589737.3605984}, keywords = {Neurorobotics, neuromorphic vision, ESIM, spiking classification, SLAYER, event-based dataset generation, industrial sockets, MuJoCO, spiking neural networks}, url = {https://doi.org/10.1145/3589737.3605984}, } @article{, author = {Angelidis, Emmanouil and Buchholz, Emanuel and Arreguit O'Neil, Jonathan Patrick and Roug{\'{e}}, Alexis and Stewart, Terrence and von Arnim, Axel and Knoll, Alois and Ijspeert, Auke}, title = {A Spiking Central Pattern Generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards}, journal = {ArXiv preprint}, year = {2021}, month = jan, abstract = {Central Pattern Generators (CPGs) models have been long used to investigate both the neural mechanisms that underlie animal locomotion as well as a tool for robotic research. In this work we propose a spiking CPG neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model. To construct our CPG model, we employ the naturally emerging dynamical systems that arise through the use of recurrent neural populations in the Neural Engineering Framework (NEF). We define the mathematical formulation behind our model, which consists of a system of coupled abstract oscillators modulated by high-level signals, capable of producing a variety of output gaits. We show that with this mathematical formulation of the Central Pattern Generator model, the model can be turned into a Spiking Neural Network (SNN) that can be easily simulated with Nengo, an SNN simulator. The spiking CPG model is then used to produce the swimming gaits of a simulated lamprey robot model in various scenarios. We show that by modifying the input to the network, which can be provided by sensory information, the robot can be controlled dynamically in direction and pace. The proposed methodology can be generalized to other types of CPGs suitable for both engineering applications and scientific research. We test our system on two neuromorphic platforms, SpiNNaker and Loihi. Finally, we show that this category of spiking algorithms shows a promising potential to exploit the theoretical advantages of neuromorphic hardware in terms of energy efficiency and computational speed.}, keywords = {neurorobotics, neuromorphic, neuromorphic computing, HBP, NRP, virtual, robotics Neuromorphic Computing, HBP, Human Brain Project, Neurorobotics, Neurorobotics Platform, Neuroscience, Artificial Intelligence, KI, Spiking Neural Networks}, url = {https://arxiv.org/abs/2101.07001}, } @article{, author = {Allegra Mascaro, Anna Letizia and Falotico, Egidio and Petkoski, Spase and Pasquini, Maria and Vannucci, Lorenzo and Tort-Colet, Nuria and Conti, Emilia and Resta, Francesco and Spalletti, Cristina and Tata Ramalingasetty, Shravan and von Arnim, Axel and Formento, Emanuele and Angelidis, Emmanouil and Blixhavn, Camilla and Leergaard, Trygve and Caleo, Matteo and Destexhe, Alain and Ijspeert, Auke and Micera, Silvestro and Laschi, Cecilia and Jirsa, Viktor and Gewaltig, Marc-Oliver and Pavone, Francesco}, title = {Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience}, journal = {Frontiers in Systems Neuroscience}, year = {2020}, month = jul, timestamp = 2020.07.07, abstract = {Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.}, doi = {https://doi.org/10.3389/fnsys.2020.00031}, keywords = {Neuromorphic Computing, HBP, Human Brain Project, Neurorobotics, Neurorobotics Platform, Neuroscience, Artificial Intelligence, KI, Spiking Neural Networks neurorobotics, neuromorphic, neuromorphic computing, HBP, NRP, virtual, robotics}, url = {https://www.frontiersin.org/articles/10.3389/fnsys.2020.00031/full}, } @article{, author = {Bornet, Alban and Kaiser, Jacques and Kroner, Alexander and Falotico, Egidio and Ambrosano, Alessandro and Cantero, Kepa and Herzog, Michael and Francis, Gregory}, title = {Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision -- The Case of Visual Crowding}, journal = {Frontiers in Neurorobotics}, year = {2019}, month = may, abstract = {Traditionally, human vision research has focused on specific paradigms and proposed models to explain very specific properties of visual perception. However, the complexity and scope of modern psychophysical paradigms undermine the success of this approach. For example, perception of an element strongly deteriorates when neighboring elements are presented in addition (visual crowding). As it was shown recently, the magnitude of deterioration depends not only on the directly neighboring elements but on almost all elements and their specific configuration. Hence, to fully explain human visual perception, one needs to take large parts of the visual field into account and combine all the aspects of vision that become relevant at such scale. These efforts require sophisticated and collaborative modeling. The Neurorobotics Platform (NRP) of the Human Brain Project offers a unique opportunity to connect models of all sorts of visual functions, even those developed by different research groups, into a coherently functioning system. Here, we describe how we used the NRP to connect and simulate a segmentation model, a retina model, and a saliency model to explain complex results about visual perception. The combination of models highlights the versatility of the NRP and provides novel explanations for inward-outward anisotropy in visual crowding.}, howpublished = {Journal}, doi = {10.3389/fnbot.2019.00033}, keywords = {robotics, human brain project, HBP, neurorobotics, neuromorphics, brain simulation, spiking neural networks, NRP, robot simulation}, url = {https://www.frontiersin.org/articles/10.3389/fnbot.2019.00033/full}, } @article{, author = {Vandersompele, Alexander and Urbain, Gabriel and Mahmud, Hossain and Wyffels, Francis and Dambre, Joni}, title = {Body Randomization Reduces the Sim-to-Real Gap for Compliant Quadruped Locomotion}, journal = {Frontiers in Neurorobotics}, year = {2019}, month = mar, abstract = {Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of physical damage. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. Here, we investigate the impact of randomizing body parameters during learning of CPG controllers in simulation. The controllers are evaluated on our physical quadruped robot. We find that body randomization in simulation increases chances of finding gaits that function well on the real robot.}, howpublished = {Journal}, doi = {10.3389/fnbot.2019.00009}, keywords = {robotics, human brain project, HBP, neurorobotics, neuromorphics, brain simulation, spiking neural networks, NRP, robot simulation}, url = {https://www.frontiersin.org/articles/10.3389/fnbot.2019.00009/full}, } @inproceedings{, author = {Weissker, Tim and Angelidis, Emmanouil and Kulik, Alexander and Beck, Stephan and Kunert, Andre and Frolov, Anton and Weber, Sandro and Kreskowski, Adrian and Froehlich, Bernd}, title = {The Collaborative Virtual Reality Neurorobotics Lab}, booktitle = {Proceedings of the {IEEE} Conference on Virtual Reality and 3D User Interfaces (VR)}, pages = {1671--1674}, year = {2019}, month = mar, address = {Osaka, Japan}, abstract = {We present the collaborative Virtual Reality Neurorobotics Lab, which allows multiple collocated and remote users to experience, discuss and participate in neurorobotic experiments in immersive virtual reality. We describe the coupling of the Neurorobotics Platform of the Human Brain Project with our collaborative virtual reality and 3D telepresence infrastructure and highlight future opportunities arising from our work for research on direct human interaction withsimulated robots and brains.}, doi = {10.1109/VR.2019.8798289}, keywords = {robotics, human brain project, HBP, neurorobotics, neuromorphics, brain simulation, spiking neural networks, NRP, robot simulation}, } @misc{, author = {Tsakiridou, Evdoxia}, title = {Roboter mit Hirn}, publisher = {Blog Innovations Report}, year = {2018}, month = mar, abstract = {fortiss hat f{\"{u}}r ein Teilprojekt des „Human Brain Project“ einen neuen Simulator entwickelt, mit dem Neurowissenschaftler ihre Hirnmodelle in virtuelle Roboter "verpflanzen" k{\"{o}}nnen. Das Besondere dabei: Die virtuellen Roboter sind mit einem Gehirn ausgestattet, das mit so genannten gepulsten neuronalen Netzen arbeitet. Diese sind ihrem biologischen Vorbild n{\"{a}}her als die aus dem maschinellen Lernen bekannten neuronalen Netze der ersten Generation. Sie versprechen eine bessere Kodierung von Nervenimpulsen und somit eine feinere Abstimmung von Bewegungen. Die Idee: Wenn Roboter sich {\"{a}}hnlich wie Menschen bewegen, k{\"{o}}nnen sie in Zukunft leichter gebaut und sicherer gesteuert werden.}, keywords = {human brain project, HBP, neurorobotics, neuromorphics, brain simulation, spiking neural networks, NRP, robot simulation}, url = {https://www.innovations-report.de/html/berichte/informationstechnologie/roboter-mit-hirn.html}, } @article{, author = {Falotico, Egidio and Vannucci, Lorenzo and Ambrosano, Alessandro and Albanese, Ugo and Ulbrich, Stefan and Vasquez Tieck, Juan Camilo and Hinkel, Georg and Kirtay, Murat and Peric, Igor and Denninger, Oliver and Cauli, Nino and Roennau, Arne and Klinker, Gudrun and von Arnim, Axel and Guyot, Luc and Peppicelli, Daniel and Martinez-Canada, Pablo and Ros, Eduardo and Maier, Patrick and Weber, Sandro and Huber, Manuel and Plecher, David and R{\"{o}}hrbein, Florian and Deser, Stefan and Roitberg, Alina and van der Smagt, Patrick and Dillmann, R{\"{u}}diger and Levi, Paul and Laschi, Cecilia and Knoll, Alois and Gewaltig, Marc-Oliver}, title = {Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform}, journal = {Frontiers in Neurorobotics}, year = {2017}, month = jan, abstract = {Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP).1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.}, doi = {10.3389/fnbot.2017.00002}, keywords = {robotics, software architectures, robot programming, web technologies, human brain project, HBP, neurorobotics, neuromorphics, brain simulation, spiking neural networks, NRP, robot simulation}, url = {https://www.frontiersin.org/articles/10.3389/fnbot.2017.00002/full}, } @inproceedings{, author = {Vannucci, Lorenzo and Ambrosano, Alessandro and Cauli, Nino and Albanese, Ugo and Falotico, Egidio and Ulbrich, Stefan and Pfotzer, Lars and Hinkel, Georg and Denninger, Oliver and Peppicelli, Daniel and Guyot, Luc and von Arnim, Axel}, title = {A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation}, booktitle = {Proceedings of the {IEEE}-{RAS} International Conference on Humanoid Robots (Humanoids)}, pages = {1179--1184}, year = {2015}, month = nov, address = {Seoul, South Korea}, abstract = {Developing neuro-inspired computing paradigms that mimic nervous system function is an emerging field of research that fosters our model understanding of the biological system and targets technical applications in artificial systems. The computational power of simulated brain circuits makes them a very promising tool for the development for brain-controlled robots. Early phases of robotic controllers development make extensive use of simulators as they are easy, fast and cheap tools. In order to develop robotics controllers that encompass brain models, a tool that include both neural simulation and physics simulation is missing. Such a tool would require the capability of orchestrating and synchronizing simulations as well as managing the exchange of data both between them. The Neurorobotics Platform (NRP) aims at filling this gap through an integrated software toolkit enabling an experimenter to design and execute a virtual experiment with a simulated robot using customized brain models. As a use case for the NRP, the iCub robot has been integrated into the platform and connected to a spiking neural network. In particular, experiments of visual tracking have been conducted in order to demonstrate the potentiality of such a platform.}, doi = {10.1109/HUMANOIDS.2015.7363512}, keywords = {robotics, human brain project, HBP, neurorobotics, neuromorphics, brain simulation, spiking neural networks, NRP, robot simulation}, language = {English}, url = {http://vislab.isr.ist.utl.pt/wp-content/uploads/2017/11/evannucci-humanoids2015.pdf}, }