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Frontiers in Neurorobotics

Poramate Manoonpong, Christian Tetzlaff
No abstract text is available yet for this article.
2018: Frontiers in Neurorobotics
Martin Biehl, Christian Guckelsberger, Christoph Salge, Simón C Smith, Daniel Polani
Active inference is an ambitious theory that treats perception, inference, and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e...
2018: Frontiers in Neurorobotics
Dongsheng Guo, Feng Xu, Laicheng Yan, Zhuoyun Nie, Hui Shao
Avoiding obstacle(s) is a challenging issue in the research of redundant robot manipulators. In addition, noise from truncation, rounding, and model uncertainty is an important factor that affects greatly the obstacle avoidance scheme. In this paper, based on the neural dynamics design formula, a new scheme with the pseudoinverse-type formulation is proposed for obstacle avoidance of redundant robot manipulators in a noisy environment. Such a scheme has the capability of suppressing constant and bounded time-varying noises, and it is thus termed as the noise-tolerant obstacle avoidance (NTOA) scheme in this paper...
2018: Frontiers in Neurorobotics
Ivan Y Tyukin, Alexander N Gorban, Konstantin I Sofeykov, Ilya Romanenko
We consider the fundamental question: how a legacy "student" Artificial Intelligent (AI) system could learn from a legacy "teacher" AI system or a human expert without re-training and, most importantly, without requiring significant computational resources. Here "learning" is broadly understood as an ability of one system to mimic responses of the other to an incoming stimulation and vice-versa. We call such learning an Artificial Intelligence knowledge transfer. We show that if internal variables of the "student" Artificial Intelligent system have the structure of an n -dimensional topological vector space and n is sufficiently high then, with probability close to one, the required knowledge transfer can be implemented by simple cascades of linear functionals...
2018: Frontiers in Neurorobotics
Muhammad Afiq Dzulkifli, Nur Azah Hamzaid, Glen M Davis, Nazirah Hasnan
This study sought to design and deploy a torque monitoring system using an artificial neural network (ANN) with mechanomyography (MMG) for situations where muscle torque cannot be independently quantified. The MMG signals from the quadriceps were used to derive knee torque during prolonged functional electrical stimulation (FES)-assisted isometric knee extensions and during standing in spinal cord injured (SCI) individuals. Three individuals with motor-complete SCI performed FES-evoked isometric quadriceps contractions on a Biodex dynamometer at 30° knee angle and at a fixed stimulation current, until the torque had declined to a minimum required for ANN model development...
2018: Frontiers in Neurorobotics
Yutaro Hiyoshi, Yuta Murai, Yoshiko Yabuki, Kenichi Takahana, Soichiro Morishita, Yinlai Jiang, Shunta Togo, Shinichiro Takayama, Hiroshi Yokoi
In this study, a one-degree-of-freedom myoelectric prosthesis system was proposed using a Parent Wireless Assistive Interface (PWAI) that allowed an external assistant (e. g., the parent of the user) to immediately adjust the parameters of the prosthetic hand controller. In the PWAI, the myoelectric potential of use of the upper limb was plotted on an external terminal in real time. Simultaneously, the assistant adjusted the parameters of the prosthetic hand control device and manually manipulated the prosthetic hand...
2018: Frontiers in Neurorobotics
Enhao Zheng, Jingeng Mai, Yuxiang Liu, Qining Wang
This study presents a noncontact capacitive sensing method for forearm motion recognition. A method is proposed to record upper limb motion information from muscle contractions without contact with human skin, compensating for the limitations of existing sEMG-based methods. The sensing front-ends are designed based on human forearm shapes, and the forearm limb shape changes caused by muscle contractions will be represented by capacitance signals. After implementation of the capacitive sensing system, experiments on healthy subjects are conducted to evaluate the effectiveness...
2018: Frontiers in Neurorobotics
Kyoung-Soub Lee, Jeong-Ho Park, Jaewon Beom, Hyung-Soon Park
As the number of people suffering from shoulder movement disabilities increases, there is a rising demand for shoulder rehabilitation. The natural motion of the shoulder joint [glenohumeral (GH) joint] includes not only three-degrees-of-freedom (DOF) rotation but also three-DOF translation of the joint center due to simultaneous motion of the shoulder girdle. If the motion of the shoulder girdle is restricted, then the arm cannot be raised above a certain posture. This paper presents a passive shoulder joint tracking device that allows three-DOF translation of the shoulder joint while compensating for gravity...
2018: Frontiers in Neurorobotics
Mathilde Legrand, Manelle Merad, Etienne de Montalivet, Agnès Roby-Brami, Nathanaël Jarrassé
Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements...
2018: Frontiers in Neurorobotics
Jan-Matthias Braun, Florentin Wörgötter, Poramate Manoonpong
Orthoses for the lower limbs support patients to perform movements that they could not perform on their own. In traditional devices, generic gait models for a limited set of supported movements restrict the patients mobility and device acceptance. To overcome such limitations, we propose a modular neural control approach with user feedback for personalizable Knee-Ankle-Foot-Orthoses (KAFO). The modular controller consists of two main neural components: neural orthosis control for gait phase tracking and neural internal models for gait prediction and selection...
2018: Frontiers in Neurorobotics
Ryoichi Nakajo, Shingo Murata, Hiroaki Arie, Tetsuya Ogata
We propose an imitative learning model that allows a robot to acquire positional relations between the demonstrator and the robot, and to transform observed actions into robotic actions. Providing robots with imitative capabilities allows us to teach novel actions to them without resorting to trial-and-error approaches. Existing methods for imitative robotic learning require mathematical formulations or conversion modules to translate positional relations between demonstrators and robots. The proposed model uses two neural networks, a convolutional autoencoder (CAE) and a multiple timescale recurrent neural network (MTRNN)...
2018: Frontiers in Neurorobotics
Payam Atoofi, Fred H Hamker, John Nassour
How do robots learn to perform motor tasks in a specific condition and apply what they have learned in a new condition? This paper proposes a framework for motor coordination acquisition of a robot drawing straight lines within a part of the workspace. Then, it addresses transferring the acquired coordination into another area of the workspace while performing the same task. Motor patterns are generated by a Central Pattern Generator (CPG) model. The motor coordination for a given task is acquired by using a multi-objective optimization method that adjusts the CPGs' parameters involved in the coordination...
2018: Frontiers in Neurorobotics
Shotaro Okajima, Maxime Tournier, Fady S Alnajjar, Mitsuhiro Hayashibe, Yasuhisa Hasegawa, Shingo Shimoda
An important function missing from current robotic systems is a human-like method for creating behavior from symbolized information. This function could be used to assess the extent to which robotic behavior is human-like because it distinguishes human motion from that of human-made machines created using currently available techniques. The purpose of this research is to clarify the mechanisms that generate automatic motor commands to achieve symbolized behavior. We design a controller with a learning method called tacit learning, which considers system-environment interactions, and a transfer method called mechanical resonance mode, which transfers the control signals into a mechanical resonance mode space (MRM-space)...
2018: Frontiers in Neurorobotics
Jeffrey L Krichmar
Neurorobots are robots whose control has been modeled after some aspect of the brain. Since the brain is so closely coupled to the body and situated in the environment, Neurorobots can be a powerful tool for studying neural function in a holistic fashion. It may also be a means to develop autonomous systems that have some level of biological intelligence. The present article provides my perspective on this field, points out some of the landmark events, and discusses its future potential.
2018: Frontiers in Neurorobotics
Bulcsú Sándor, Michael Nowak, Tim Koglin, Laura Martin, Claudius Gros
Self-organized robots may develop attracting states within the sensorimotor loop, that is within the phase space of neural activity, body and environmental variables. Fixpoints, limit cycles and chaotic attractors correspond in this setting to a non-moving robot, to directed, and to irregular locomotion respectively. Short higher-order control commands may hence be used to kick the system from one self-organized attractor robustly into the basin of attraction of a different attractor, a concept termed here as kick control...
2018: Frontiers in Neurorobotics
Maziar A Sharbafi, Hamid Barazesh, Majid Iranikhah, Andre Seyfarth
Assistive devices can be considered as one of the main applications of legged locomotion research in daily life. In order to develop an efficient and comfortable prosthesis or exoskeleton, biomechanical studies on human locomotion are very useful. In this paper, the applicability of the FMCH (force modulated compliant hip) model is investigated for control of lower limb wearable exoskeletons. This is a bioinspired method for posture control, which is based on the virtual pivot point (VPP) concept, found in human walking...
2018: Frontiers in Neurorobotics
Giulio Sandini, Vishwanathan Mohan, Alessandra Sciutti, Pietro Morasso
The next generation of robot companions or robot working partners will need to satisfy social requirements somehow similar to the famous laws of robotics envisaged by Isaac Asimov time ago (Asimov, 1942). The necessary technology has almost reached the required level, including sensors and actuators, but the cognitive organization is still in its infancy and is only partially supported by the current understanding of brain cognitive processes. The brain of symbiotic robots will certainly not be a "positronic" replica of the human brain: probably, the greatest part of it will be a set of interacting computational processes running in the cloud...
2018: Frontiers in Neurorobotics
Zhenshan Bing, Claus Meschede, Florian Röhrbein, Kai Huang, Alois C Knoll
Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains...
2018: Frontiers in Neurorobotics
Ruth Schulz, Philipp Kratzer, Marc Toussaint
How do humans want to interact with collaborative robots? As robots become more common and useful not only in industry but also in the home, they will need to interact with humans to complete many varied tasks. Previous studies have demonstrated that autonomous robots are often more efficient and preferred over those that need to be commanded, or those that give instructions to humans. We believe that the types of actions that make up a task affect the preference of participants for different interaction styles...
2018: Frontiers in Neurorobotics
Francesco Mannella, Vieri G Santucci, Eszter Somogyi, Lisa Jacquey, Kevin J O'Regan, Gianluca Baldassarre
The first "object" that newborn children play with is their own body. This activity allows them to autonomously form a sensorimotor map of their own body and a repertoire of actions supporting future cognitive and motor development. Here we propose the theoretical hypothesis, operationalized as a computational model, that this acquisition of body knowledge is not guided by random motor-babbling, but rather by autonomously generated goals formed on the basis of intrinsic motivations. Motor exploration leads the agent to discover and form representations of the possible sensory events it can cause with its own actions...
2018: Frontiers in Neurorobotics
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