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Frontiers in Neurorobotics

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https://www.readbyqxmd.com/read/28420977/development-and-training-of-a-neural-controller-for-hind-leg-walking-in-a-dog-robot
#1
Alexander Hunt, Nicholas Szczecinski, Roger Quinn
Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal's body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few "synthetic nervous systems" have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28420976/improving-robot-motor-learning-with-negatively-valenced-reinforcement-signals
#2
Nicolás Navarro-Guerrero, Robert J Lowe, Stefan Wermter
Both nociception and punishment signals have been used in robotics. However, the potential for using these negatively valenced types of reinforcement learning signals for robot learning has not been exploited in detail yet. Nociceptive signals are primarily used as triggers of preprogrammed action sequences. Punishment signals are typically disembodied, i.e., with no or little relation to the agent-intrinsic limitations, and they are often used to impose behavioral constraints. Here, we provide an alternative approach for nociceptive signals as drivers of learning rather than simple triggers of preprogrammed behavior...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28396634/morphological-properties-of-mass-spring-networks-for-optimal-locomotion-learning
#3
Gabriel Urbain, Jonas Degrave, Benonie Carette, Joni Dambre, Francis Wyffels
Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28381998/real-time-biologically-inspired-action-recognition-from-key-poses-using-a-neuromorphic-architecture
#4
Georg Layher, Tobias Brosch, Heiko Neumann
Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28377710/fast-dynamical-coupling-enhances-frequency-adaptation-of-oscillators-for-robotic-locomotion-control
#5
Timo Nachstedt, Christian Tetzlaff, Poramate Manoonpong
Rhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and coordinate the produced rhythmic activity, CPGs require sensory feedback, i.e., external signals. Nonlinear oscillators are a standard model of CPGs and are used in various robotic applications. A special class of nonlinear oscillators are adaptive frequency oscillators (AFOs)...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28367121/experimental-validation-of-motor-primitive-based-control-for-leg-exoskeletons-during-continuous-multi-locomotion-tasks
#6
Virginia Ruiz Garate, Andrea Parri, Tingfang Yan, Marko Munih, Raffaele Molino Lova, Nicola Vitiello, Renaud Ronsse
An emerging approach to design locomotion assistive devices deals with reproducing desirable biological principles of human locomotion. In this paper, we present a bio-inspired controller for locomotion assistive devices based on the concept of motor primitives. The weighted combination of artificial primitives results in a set of virtual muscle stimulations. These stimulations then activate a virtual musculoskeletal model producing reference assistive torque profiles for different locomotion tasks (i.e., walking, ascending stairs, and descending stairs)...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28360852/self-organized-behavior-generation-for-musculoskeletal-robots
#7
Ralf Der, Georg Martius
With the accelerated development of robot technologies, control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unknown dynamics or which are difficult to model. Reasons are the expected scalability, robustness, and resilience of self-organizing systems...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28337138/motor-skill-learning-in-an-insect-inspired-neuro-computational-control-system
#8
Eleonora Arena, Paolo Arena, Roland Strauss, Luca Patané
In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28337137/an-adaptive-neural-mechanism-for-acoustic-motion-perception-with-varying-sparsity
#9
Danish Shaikh, Poramate Manoonpong
Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28303100/a-neural-dynamic-architecture-for-reaching-and-grasping-integrates-perception-and-movement-generation-and-enables-on-line-updating
#10
Guido Knips, Stephan K U Zibner, Hendrik Reimann, Gregor Schöner
Reaching for objects and grasping them is a fundamental skill for any autonomous robot that interacts with its environment. Although this skill seems trivial to adults, who effortlessly pick up even objects they have never seen before, it is hard for other animals, for human infants, and for most autonomous robots. Any time during movement preparation and execution, human reaching movement are updated if the visual scene changes (with a delay of about 100 ms). The capability for online updating highlights how tightly perception, movement planning, and movement generation are integrated in humans...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28261085/translating-research-on-myoelectric-control-into-clinics-are-the-performance-assessment-methods-adequate
#11
Ivan Vujaklija, Aidan D Roche, Timothy Hasenoehrl, Agnes Sturma, Sebastian Amsuess, Dario Farina, Oskar C Aszmann
Missing an upper limb dramatically impairs daily-life activities. Efforts in overcoming the issues arising from this disability have been made in both academia and industry, although their clinical outcome is still limited. Translation of prosthetic research into clinics has been challenging because of the difficulties in meeting the necessary requirements of the market. In this perspective article, we suggest that one relevant factor determining the relatively small clinical impact of myocontrol algorithms for upper limb prostheses is the limit of commonly used laboratory performance metrics...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28261084/hybrid-eeg-fnirs-based-eight-command-decoding-for-bci-application-to-quadcopter-control
#12
Muhammad Jawad Khan, Keum-Shik Hong
In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28223931/a-computational-model-for-spatial-navigation-based-on-reference-frames-in-the-hippocampus-retrosplenial-cortex-and-posterior-parietal-cortex
#13
Timo Oess, Jeffrey L Krichmar, Florian Röhrbein
Behavioral studies for humans, monkeys, and rats have shown that, while traversing an environment, these mammals tend to use different frames of reference and frequently switch between them. These frames represent allocentric, egocentric, or route-centric views of the environment. However, combinations of either of them are often deployed. Neurophysiological studies on rats have indicated that the hippocampus, the retrosplenial cortex, and the posterior parietal cortex contribute to the formation of these frames and mediate the transformation between those...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28217092/neurodynamics-in-the-sensorimotor-loop-representing-behavior-relevant-external-situations
#14
Frank Pasemann
In the context of the dynamical system approach to cognition and supposing that brains or brain-like systems controlling the behavior of autonomous systems are permanently driven by their sensor signals, the paper approaches the question of neurodynamics in the sensorimotor loop in a purely formal way. This is carefully done by addressing the problem in three steps, using the time-discrete dynamics of standard neural networks and a fiber space representation for better clearness. Furthermore, concepts like meta-transients, parametric stability and dynamical forms are introduced, where meta-transients describe the effect of realistic sensor inputs, parametric stability refers to a class of sensor inputs all generating the "same type" of dynamic behavior, and a dynamical form comprises the corresponding class of parametrized dynamical systems...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28194106/reacog-a-minimal-cognitive-controller-based-on-recruitment-of-reactive-systems
#15
Malte Schilling, Holk Cruse
It has often been stated that for a neuronal system to become a cognitive one, it has to be large enough. In contrast, we argue that a basic property of a cognitive system, namely the ability to plan ahead, can already be fulfilled by small neuronal systems. As a proof of concept, we propose an artificial neural network, termed reaCog, that, first, is able to deal with a specific domain of behavior (six-legged-walking). Second, we show how a minor expansion of this system enables the system to plan ahead and deploy existing behavioral elements in novel contexts in order to solve current problems...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28179882/connecting-artificial-brains-to-robots-in-a-comprehensive-simulation-framework-the-neurorobotics-platform
#16
Egidio Falotico, Lorenzo Vannucci, Alessandro Ambrosano, Ugo Albanese, Stefan Ulbrich, Juan Camilo Vasquez Tieck, Georg Hinkel, Jacques Kaiser, Igor Peric, Oliver Denninger, Nino Cauli, Murat Kirtay, Arne Roennau, Gudrun Klinker, Axel Von Arnim, Luc Guyot, Daniel Peppicelli, Pablo Martínez-Cañada, Eduardo Ros, Patrick Maier, Sandro Weber, Manuel Huber, David Plecher, Florian Röhrbein, Stefan Deser, Alina Roitberg, Patrick van der Smagt, Rüdiger Dillman, Paul Levi, Cecilia Laschi, Alois C Knoll, Marc-Oliver Gewaltig
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...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28167910/adaptive-baseline-enhances-em-based-policy-search-validation-in-a-view-based-positioning-task-of-a-smartphone-balancer
#17
Jiexin Wang, Eiji Uchibe, Kenji Doya
EM-based policy search methods estimate a lower bound of the expected return from the histories of episodes and iteratively update the policy parameters using the maximum of a lower bound of expected return, which makes gradient calculation and learning rate tuning unnecessary. Previous algorithms like Policy learning by Weighting Exploration with the Returns, Fitness Expectation Maximization, and EM-based Policy Hyperparameter Exploration implemented the mechanisms to discard useless low-return episodes either implicitly or using a fixed baseline determined by the experimenter...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28066228/is-the-prosthetic-homologue-necessary-for-embodiment
#18
Chelsea Dornfeld, Michelle Swanston, Joseph Cassella, Casey Beasley, Jacob Green, Yonatan Moshayev, Michael Wininger
Embodiment is the process by which patients with limb loss come to accept their peripheral device as a natural extension of self. However, there is little guidance as to how exacting the prosthesis must be in order for embodiment to take place: is it necessary for the prosthetic hand to look just like the absent hand? Here, we describe a protocol for testing whether an individual would select a hand that looks like their own from among a selection of five hands, and whether the hand selection (regardless of homology) is consistent across multiple exposures to the same (but reordered) set of candidate hands...
2016: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/27999538/robotic-and-virtual-reality-bcis-using-spatial-tactile-and-auditory-oddball-paradigms
#19
REVIEW
Tomasz M Rutkowski
The paper reviews nine robotic and virtual reality (VR) brain-computer interface (BCI) projects developed by the author, in collaboration with his graduate students, within the BCI-lab research group during its association with University of Tsukuba, Japan. The nine novel approaches are discussed in applications to direct brain-robot and brain-virtual-reality-agent control interfaces using tactile and auditory BCI technologies. The BCI user intentions are decoded from the brainwaves in realtime using a non-invasive electroencephalography (EEG) and they are translated to a symbiotic robot or virtual reality agent thought-based only control...
2016: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/27965567/biomechanical-reconstruction-using-the-tacit-learning-system-intuitive-control-of-prosthetic-hand-rotation
#20
Shintaro Oyama, Shingo Shimoda, Fady S K Alnajjar, Katsuyuki Iwatsuki, Minoru Hoshiyama, Hirotaka Tanaka, Hitoshi Hirata
Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals...
2016: Frontiers in Neurorobotics
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