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Frontiers in Neurorobotics

Wubing Fang, Fei Chao, Chih-Min Lin, Longzhi Yang, Changjing Shang, Changle Zhou
The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots...
2019: Frontiers in Neurorobotics
Matteo Bianchi, Gionata Salvietti
No abstract text is available yet for this article.
2019: Frontiers in Neurorobotics
Yen Yu, Acer Y C Chang, Ryota Kanai
This paper presents the Homeo-Heterostatic Value Gradients (HHVG) algorithm as a formal account on the constructive interplay between boredom and curiosity which gives rise to effective exploration and superior forward model learning. We offer an instrumental view of action selection, in which an action serves to disclose outcomes that have intrinsic meaningfulness to an agent itself. This motivated two central algorithmic ingredients: devaluation and devaluation progress, both underpin agent's cognition concerning intrinsically generated rewards...
2018: Frontiers in Neurorobotics
Salvatore Tramonte, Rosario Sorbello, Christopher Guger, Antonio Chella
In this paper, authors present a novel architecture for controlling an industrial robot via Brain Computer Interface. The robot used is a Series 2000 KR 210-2. The robotic arm was fitted with DI drawing devices that clamp, hold and manipulate various artistic media like brushes, pencils, pens. User selected a high-level task, for instance a shape or movement, using a human machine interface and the translation in robot movement was entirely demanded to the Robot Control Architecture defining a plan to accomplish user's task...
2018: Frontiers in Neurorobotics
Nicolas Duminy, Sao Mai Nguyen, Dominique Duhaut
We aim at a robot capable to learn sequences of actions to achieve a field of complex tasks. In this paper, we are considering the learning of a set of interrelated complex tasks hierarchically organized. To learn this high-dimensional mapping between a continuous high-dimensional space of tasks and an infinite dimensional space of unbounded sequences of actions, we introduce a new framework called "procedures", which enables the autonomous discovery of how to combine previously learned skills in order to learn increasingly complex combinations of motor policies...
2018: Frontiers in Neurorobotics
Ángel Manuel Guerrero-Higueras, Claudia Álvarez-Aparicio, María Carmen Calvo Olivera, Francisco J Rodríguez-Lera, Camino Fernández-Llamas, Francisco Martín Rico, Vicente Matellán
Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc...
2018: Frontiers in Neurorobotics
Visar Arapi, Cosimo Della Santina, Davide Bacciu, Matteo Bianchi, Antonio Bicchi
Humans are capable of complex manipulation interactions with the environment, relying on the intrinsic adaptability and compliance of their hands. Recently, soft robotic manipulation has attempted to reproduce such an extraordinary behavior, through the design of deformable yet robust end-effectors. To this goal, the investigation of human behavior has become crucial to correctly inform technological developments of robotic hands that can successfully exploit environmental constraint as humans actually do. Among the different tools robotics can leverage on to achieve this objective, deep learning has emerged as a promising approach for the study and then the implementation of neuro-scientific observations on the artificial side...
2018: Frontiers in Neurorobotics
Philipp Beckerle, Risto Kõiva, Elsa Andrea Kirchner, Robin Bekrater-Bodmann, Strahinja Dosen, Oliver Christ, David A Abbink, Claudio Castellini, Bigna Lenggenhager
The feeling of embodiment, i.e., experiencing the body as belonging to oneself and being able to integrate objects into one's bodily self-representation, is a key aspect of human self-consciousness and has been shown to importantly shape human cognition. An extension of such feelings toward robots has been argued as being crucial for assistive technologies aiming at restoring, extending, or simulating sensorimotor functions. Empirical and theoretical work illustrates the importance of sensory feedback for the feeling of embodiment and also immersion; we focus on the the perceptual level of touch and the role of tactile feedback in various assistive robotic devices...
2018: Frontiers in Neurorobotics
Emmanuel Daucé
What motivates an action in the absence of a definite reward? Taking the case of visuomotor control, we consider a minimal control problem that is how select the next saccade, in a sequence of discrete eye movements, when the final objective is to better interpret the current visual scene. The visual scene is modeled here as a partially-observed environment, with a generative model explaining how the visual data is shaped by action. This allows to interpret different action selection metrics proposed in the literature, including the Salience, the Infomax and the Variational Free Energy, under a single information theoretic construct, namely the view-based Information Gain...
2018: Frontiers in Neurorobotics
Louis Flynn, Joost Geeroms, Rene Jimenez-Fabian, Sophie Heins, Bram Vanderborght, Marko Munih, Raffaele Molino Lova, Nicola Vitiello, Dirk Lefeber
The CYBERLEGs Beta-Prosthesis is an active transfemoral prosthesis that can provide the full torque required for reproducing average level ground walking at both the knee and ankle in the sagittal plane. The prosthesis attempts to produce a natural level ground walking gait that approximates the joint torques and kinematics of a non-amputee while maintaining passively compliant joints, the stiffnesses of which were derived from biological quasi-stiffness measurements. The ankle of the prosthesis consists of a series elastic actuator with a parallel spring and the knee is composed of three different systems that must compliment each other to generate the correct joint behavior: a series elastic actuator, a lockable parallel spring and an energy transfer mechanism...
2018: Frontiers in Neurorobotics
Victoria Alonso, Paloma de la Puente
What does transparency mean in a shared autonomy framework? Different ways of understanding system transparency in human-robot interaction can be found in the state of the art. In one of the most common interpretations of the term, transparency is the observability and predictability of the system behavior, the understanding of what the system is doing, why, and what it will do next. Since the main methods to improve this kind of transparency are based on interface design and training, transparency is usually considered a property of such interfaces, while natural language explanations are a popular way to achieve transparent interfaces...
2018: Frontiers in Neurorobotics
Jun Xie, Guanghua Xu, Xingang Zhao, Min Li, Jing Wang, Chengcheng Han, Xingliang Han
Neuroplasticity, also known as brain plasticity, is an inclusive term that covers the permanent changes in the brain during the course of an individual's life, and neuroplasticity can be broadly defined as the changes in function or structure of the brain in response to the external and/or internal influences. Long-term potentiation (LTP), a well-characterized form of functional synaptic plasticity, could be influenced by rapid-frequency stimulation (or "tetanus") within in vivo human sensory pathways...
2018: Frontiers in Neurorobotics
German I Parisi, Jun Tani, Cornelius Weber, Stefan Wermter
Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting in which novel sensory experience interferes with existing representations and leads to abrupt decreases in the performance on previously acquired knowledge...
2018: Frontiers in Neurorobotics
Christian Huyck, Ian Mitchell
The best way to develop a Turing test passing AI is to follow the human model: an embodied agent that functions over a wide range of domains, is a human cognitive model, follows human neural functioning and learns. These properties will endow the agent with the deep semantics required to pass the test. An embodied agent functioning over a wide range of domains is needed to be exposed to and learn the semantics of those domains. Following human cognitive and neural functioning simplifies the search for sufficiently sophisticated mechanisms by reusing mechanisms that are already known to be sufficient...
2018: Frontiers in Neurorobotics
Alexander I Kostyukov, Tomasz Tomiak
The two-segment model of the human arm is considered; the shoulder and elbow joint torques (JTs) are simulated, providing a slow, steady rotation of the force vector at any end-point of the horizontal working space. The sinusoidal waves describe the JTs, their periods coincide with that of the rotation, and phases are defined by the slopes of the correspondent lines from the joint axes to the end-point. Analysis of the JTs includes an application of the same discrete changes in one joint angle under fixation of the other one and vice versa; the JT pairs are compared for the "shoulder" and "elbow" end-point traces that pass under fixation of the elbow and shoulder angles, respectively...
2018: Frontiers in Neurorobotics
André Cyr, Frédéric Thériault, Matthew Ross, Nareg Berberian, Sylvain Chartier
Visual motion detection is essential for the survival of many species. The phenomenon includes several spatial properties, not fully understood at the level of a neural circuit. This paper proposes a computational model of a visual motion detector that integrates direction and orientation selectivity features. A recent experiment in the Drosophila model highlights that stimulus orientation influences the neural response of direction cells. However, this interaction and the significance at the behavioral level are currently unknown...
2018: Frontiers in Neurorobotics
Domenico Buongiorno, Michele Barsotti, Francesco Barone, Vitoantonio Bevilacqua, Antonio Frisoli
The growing interest of the industry production in wearable robots for assistance and rehabilitation purposes opens the challenge for developing intuitive and natural control strategies. Myoelectric control, or myo-control, which consists in decoding the human motor intent from muscular activity and its mapping into control outputs, represents a natural way to establish an intimate human-machine connection. In this field, model based myo-control schemes (e.g., EMG-driven neuromusculoskeletal models, NMS) represent a valid solution for estimating the moments of the human joints...
2018: Frontiers in Neurorobotics
Zhijun Zhang, Qiongyi Zhou, Weisen Fan
In order to track complex-path tasks in three dimensional space without joint-drifts, a neural-dynamic based synchronous-optimization (NDSO) scheme of dual redundant robot manipulators is proposed and developed. To do so, an acceleration-level repetitive motion planning optimization criterion is derived by the neural-dynamic method twice. Position and velocity feedbacks are taken into account to decrease the errors. Considering the joint-angle, joint-velocity, and joint-acceleration limits, the redundancy resolution problem of the left and right arms are formulated as two quadratic programming problems subject to equality constraints and three bound constraints...
2018: Frontiers in Neurorobotics
Ameer Hamza Khan, Shuai Li, Xuefeng Zhou, Yangming Li, Muhammad Umer Khan, Xin Luo, Huanqing Wang
No abstract text is available yet for this article.
2018: Frontiers in Neurorobotics
Tatsuya Teramae, Koji Ishihara, Jan Babič, Jun Morimoto, Erhan Oztop
Pneumatically actuated muscles (PAMs) provide a low cost, lightweight, and high power-to-weight ratio solution for many robotic applications. In addition, the antagonist pair configuration for robotic arms make it open to biologically inspired control approaches. In spite of these advantages, they have not been widely adopted in human-in-the-loop control and learning applications. In this study, we propose a biologically inspired multimodal human-in-the-loop control system for driving a one degree-of-freedom robot, and realize the task of hammering a nail into a wood block under human control...
2018: Frontiers in Neurorobotics
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