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Frontiers in Neurorobotics

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https://www.readbyqxmd.com/read/28503145/a-neural-dynamic-architecture-for-concurrent-estimation-of-object-pose-and-identity
#1
Oliver Lomp, Christian Faubel, Gregor Schöner
Handling objects or interacting with a human user about objects on a shared tabletop requires that objects be identified after learning from a small number of views and that object pose be estimated. We present a neurally inspired architecture that learns object instances by storing features extracted from a single view of each object. Input features are color and edge histograms from a localized area that is updated during processing. The system finds the best-matching view for the object in a novel input image while concurrently estimating the object's pose, aligning the learned view with current input...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28487646/human-inspired-eigenmovement-concept-provides-coupling-free-sensorimotor-control-in-humanoid-robot
#2
Alexei V Alexandrov, Vittorio Lippi, Thomas Mergner, Alexander A Frolov, Georg Hettich, Dusan Husek
Control of a multi-body system in both robots and humans may face the problem of destabilizing dynamic coupling effects arising between linked body segments. The state of the art solutions in robotics are full state feedback controllers. For human hip-ankle coordination, a more parsimonious and theoretically stable alternative to the robotics solution has been suggested in terms of the Eigenmovement (EM) control. Eigenmovements are kinematic synergies designed to describe the multi DoF system, and its control, with a set of independent, and hence coupling-free, scalar equations...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28450834/communication-and-inference-of-intended-movement-direction-during-human-human-physical-interaction
#3
Keivan Mojtahedi, Bryan Whitsell, Panagiotis Artemiadis, Marco Santello
Of particular interest to the neuroscience and robotics communities is the understanding of how two humans could physically collaborate to perform motor tasks such as holding a tool or moving it across locations. When two humans physically interact with each other, sensory consequences and motor outcomes are not entirely predictable as they also depend on the other agent's actions. The sensory mechanisms involved in physical interactions are not well understood. The present study was designed (1) to quantify human-human physical interactions where one agent ("follower") has to infer the intended or imagined-but not executed-direction of motion of another agent ("leader") and (2) to reveal the underlying strategies used by the dyad...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28446872/a-neurocomputational-model-of-goal-directed-navigation-in-insect-inspired-artificial-agents
#4
Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta
Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28443015/cross-subject-eeg-feature-selection-for-emotion-recognition-using-transfer-recursive-feature-elimination
#5
Zhong Yin, Yongxiong Wang, Li Liu, Wei Zhang, Jianhua Zhang
Using machine-learning methodologies to analyze EEG signals becomes increasingly attractive for recognizing human emotions because of the objectivity of physiological data and the capability of the learning principles on modeling emotion classifiers from heterogeneous features. However, the conventional subject-specific classifiers may induce additional burdens to each subject for preparing multiple-session EEG data as training sets. To this end, we developed a new EEG feature selection approach, transfer recursive feature elimination (T-RFE), to determine a set of the most robust EEG indicators with stable geometrical distribution across a group of training subjects and a specific testing subject...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28420977/development-and-training-of-a-neural-controller-for-hind-leg-walking-in-a-dog-robot
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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