@article{Falotico2017, author = { Egidio Falotico and Lorenzo Vannucci and Alessandro Ambrosano and Ugo Albanese and Stefan Ulbrich and Juan Camilo Vasquez Tieck and Georg Hinkel and Jacques Kaiser and Igor Peric and Oliver Denninger and Nino Cauli et al.}, title = {Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform}, journal = {Frontiers in Neurorobotics}, year = {2017}, volume = {11}, pages = {2}, url = {https://www.frontiersin.org/article/10.3389/fnbot.2017.00002}, 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 filling 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, envi-ronments, 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). 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.} } @article{hinkel2017framework, author = {Hinkel, Georg and Groenda, Henning and Krach, Sebastian and Vannucci, Lorenzo and Denninger, Oliver and Cauli, Nino and Ulbrich, Stefan and Roennau, Arne and Falotico, Egidio and Gewaltig, Marc-Oliver and others}, title = {A framework for coupled simulations of robots and spiking neuronal networks}, journal = {Journal of Intelligent \& Robotic Systems}, year = {2017}, volume = {85}, number = {1}, pages = {71--91}, publisher = {Springer} } @article{Pfotzer2017, author = {L. Pfotzer and S. Klemm and A. Roennau and J.M. Zöllner and R. Dillmann}, title = {Autonomous navigation for reconfigurable snake-like robots in challenging, unknown environments}, journal = {Robotics and Autonomous Systems}, year = {2017}, volume = {89}, pages = {123 - 135}, url = {http://www.sciencedirect.com/science/article/pii/S0921889016300549} } @article{kaiser2017lsm, author = {Jacques Kaiser and Rainer Stal and Anand Subramoney and Arne Roennau and Rüdiger Dillmann}, title = {Scaling up liquid state machines to predict over address events from dynamic vision sensors}, journal = {Bioinspiration \& Biomimetics}, year = {2017}, volume = {12}, number = {5}, month = {September}, note = {Special issue on NeuroRobotics: Brain-inspired models for robot control and behaviour}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28569669} } @inproceedings{Juelg2017, author = {C. Juelg and A. Hermann and A. Roennau and R. Dillmann}, title = {Fast online collision avoidance for mobile service robots through potential fields on 3D environment data processed on GPUs}, year = {2017}, booktitle = {2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)} } @inproceedings{Kaiser2017b, author = {Kaiser, Jacques and Zimmerer, David and Tieck, J. Camilo Vasquez and Ulbrich, Stefan and Roennau, Arne and Dillmann, Rüdiger}, title = {Spiking Convolutional Deep Belief Networks}, year = {2017}, pages = {3--11}, editor = {Lintas, Alessandra and Rovetta, Stefano and Verschure, Paul F.M.J. and Villa, Alessandro E.P.}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-68612-7}, booktitle = {Artificial Neural Networks and Machine Learning -- ICANN 2017}, abstract = {Understanding visual input as perceived by humans is a challenging task for machines. Today, most successful methods work by learning features from static images. Based on classical artificial neural networks, those methods are not adapted to process event streams as provided by the Dynamic Vision Sensor (DVS). Recently, an unsupervised learning rule to train Spiking Restricted Boltzmann Machines has been presented [9]. Relying on synaptic plasticity, it can learn features directly from event streams. In this paper, we extend this method by adding convolutions, lateral inhibitions and multiple layers. We evaluate our method on a self-recorded DVS dataset as well as the Poker-DVS dataset. Our results show that our convolutional method performs better and needs less parameters. It also achieves comparable results to previous event-based classification methods while learning features in an unsupervised fashion.} } @inproceedings{peric2017a, author = {Igor Peric and Alexandru Lesi and Daniel Spies and Stefan Ulbrich and Arne Roennau and Marius Zoellner and Ruediger Dillmann}, title = {Probabilistic Symbol Encoding for Convolutional Associative Memories}, year = {2017}, pages = {22--29}, booktitle = {Proceedings of the 5th International Congress on Neurotechnology, Electronics and Informatics, NEUROTECHNIX 2017, Funchal, Madeira, Portugal, October 30-31, 2017.}, url = {https://doi.org/10.5220/0006503000220029} } @inproceedings{Peric2017b, author = {I. Peric and F. Schneider and C. H. Price and S. Ulbrich and A. Roennau and M. Zoellner and R. Dillmann}, title = {Exact spike timing computational model of convolutional associative memories}, year = {2017}, pages = {182-190}, month = {July}, booktitle = {2017 IEEE 16th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC)} } @inproceedings{roennau2017, author = {A. Roennau}, title = {Mensch-Roboter-Kollaboration von der Forschung in die Praxis}, year = {2017}, booktitle = {2. VDI-Fachkonferenz Humanoide Roboter} } @inproceedings{Tieck2017a, author = {Tieck, J. Camilo Vasquez and Donat, Heiko and Kaiser, Jacques and Peric, Igor and Ulbrich, Stefan and Roennau, Arne and Zöllner, Marius and Dillmann, Rüdiger}, title = {Towards Grasping with Spiking Neural Networks for Anthropomorphic Robot Hands}, year = {2017}, pages = {43--51}, editor = {Lintas, Alessandra and Rovetta, Stefano and Verschure, Paul F.M.J. and Villa, Alessandro E.P.}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-68600-4}, booktitle = {Artificial Neural Networks and Machine Learning -- ICANN 2017}, abstract = {Representation and execution of movement in biology is an active field of research relevant to neurorobotics. Humans can remember grasp motions and modify them during execution based on the shape and the intended interaction with objects. We present a hierarchical spiking neural network with a biologically inspired architecture for representing different grasp motions. We demonstrate the ability of our network to learn from human demonstration using synaptic plasticity on two different exemplary grasp types (pinch and cylinder). We evaluate the performance of the network in simulation and on a real anthropomorphic robotic hand. The network exposes the ability of learning finger coordination and synergies between joints that can be used for grasping.} } @inproceedings{Ulbrich2017, author = {S. Ulbrich and T. Steward and I. Peri? and A. Roennau and J. M. Zöllner and R. Dillmann}, title = {Model-based polynomial function approximation with spiking neural networks}, year = {2017}, pages = {22-27}, month = {July}, booktitle = {2017 IEEE 16th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC)} } @inproceedings{Buettner2017, author = {T. Buettner and A. Roennau and G. Heppner and R. Dillmann}, title = {Design of an exchangeable, compact and modular bio-inspired leg for six-legged walking robots ROBOTS}, year = {2017}, pages = {89-96}, booktitle = {Human-Centric Robotics - Proceedings Of The 20th International Conference Clawar 2017}, url = {https://www.worldscientific.com/doi/abs/10.1142/9789813231047_0014} } @inproceedings{Grosse_Besselmann2017, author = {M. Grosse Besselmann and A. Roennau and R. Dillmann}, title = {Model-based Localisation and Segmentation of Modular Satellites using 3D Lidar Point Clouds}, year = {2017}, booktitle = {68th International Astronautical Congress of the International Astronautical Federation, the International Academy of Astronautics, and the International Institute of Space Law (IAC)} } @inproceedings{Heppner2017, author = {G. Heppner and A. Roennau and R. Dillmann}, title = {Exploration and Sample-Return Missions with a Walking Robot}, year = {2017}, booktitle = {2017 Proceedings of the 2017 ESA International Conference on Automation and Robotics in Space (ASTRA)} } @inproceedings{Mauch2017, author = {F. Mauch and A. Roennau and G. Heppner and T. Buettner and R. Dillmann}, title = {Service robots in the field: The BratWurst Bot}, year = {2017}, pages = {13-19}, month = {July}, booktitle = {2017 18th International Conference on Advanced Robotics (ICAR)} } @inproceedings{8023505, author = {J. Mangler and F. Mauch and A. Roennau and R. Dillmann}, title = {Predictive motion synchronization for two arm dynamic mobile manipulation}, year = {2017}, pages = {118-123}, month = {July}, booktitle = {2017 18th International Conference on Advanced Robotics (ICAR)} } @inproceedings{Scherzinger2017, author = {S. Scherzinger and A. Roennau and R. Dillmann}, title = {Forward Dynamics Compliance Control (FDCC): A new approach to cartesian compliance for robotic manipulators}, year = {2017}, pages = {4568-4575}, month = {Sept}, booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)} }