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Frontiers in Neurorobotics

Tomoaki Nakamura, Takayuki Nagai, Tadahiro Taniguchi
To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand their environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inferences easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environment and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence...
2018: Frontiers in Neurorobotics
Suresh Kumar, Patricia Shaw, Alexandros Giagkos, Raphäel Braud, Mark Lee, Qiang Shen
Examining the different stages of learning through play in humans during early life has been a topic of interest for various scholars. Play evolves from practice to symbolic and then later to play with rules. During practice play, infants go through a process of developing knowledge while they interact with the surrounding objects, facilitating the creation of new knowledge about objects and object related behaviors. Such knowledge is used to form schemas in which the manifestation of sensorimotor experiences is captured...
2018: Frontiers in Neurorobotics
Thommen George Karimpanal, Roland Bouffanais
Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms. In this work, we propose an approach to select and replay sequences of transitions in order to accelerate the learning of a reinforcement learning agent in an off-policy setting. In addition to selecting appropriate sequences, we also artificially construct transition sequences using information gathered from previous agent-environment interactions. These sequences, when replayed, allow value function information to trickle down to larger sections of the state/state-action space, thereby making the most of the agent's experience...
2018: Frontiers in Neurorobotics
Melanie Jouaiti, Lancelot Caron, Patrick Hénaff
It is well-known that human social interactions generate synchrony phenomena which are often unconscious. If the interaction between individuals is based on rhythmic movements, synchronized and coordinated movements will emerge from the social synchrony. This paper proposes a plausible model of plastic neural controllers that allows the emergence of synchronized movements in physical and rhythmical interactions. The controller is designed with central pattern generators (CPG) based on rhythmic Rowat-Selverston neurons endowed with neuronal and synaptic Hebbian plasticity...
2018: Frontiers in Neurorobotics
Mihai Andries, Ricardo Omar Chavez-Garcia, Raja Chatila, Alessandro Giusti, Luca Maria Gambardella
Automatic knowledge grounding is still an open problem in cognitive robotics. Recent research in developmental robotics suggests that a robot's interaction with its environment is a valuable source for collecting such knowledge about the effects of robot's actions. A useful concept for this process is that of an affordance, defined as a relationship between an actor, an action performed by this actor, an object on which the action is performed, and the resulting effect. This paper proposes a formalism for defining and identifying affordance equivalence...
2018: Frontiers in Neurorobotics
Olivier White, Amir Karniel, Charalambos Papaxanthis, Marie Barbiero, Ilana Nisky
Switched systems are common in artificial control systems. Here, we suggest that the brain adopts a switched feedforward control of grip forces during manipulation of objects. We measured how participants modulated grip force when interacting with soft and rigid virtual objects when stiffness varied continuously between trials. We identified a sudden phase transition between two forms of feedforward control that differed in the timing of the synchronization between the anticipated load force and the applied grip force...
2018: Frontiers in Neurorobotics
Gionata Salvietti
This review reports the principal solutions proposed in the literature to reduce the complexity of the control and of the design of robotic hands taking inspiration from the organization of the human brain. Several studies in neuroscience concerning the sensorimotor organization of the human hand proved that, despite the complexity of the hand, a few parameters can describe most of the variance in the patterns of configurations and movements. In other words, humans exploit a reduced set of parameters, known in the literature as synergies, to control their hands...
2018: Frontiers in Neurorobotics
Lan Anh Trinh, Mikael Ekström, Baran Cürüklü
Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i...
2018: Frontiers in Neurorobotics
Marco Ewerton, David Rother, Jakob Weimar, Gerrit Kollegger, Josef Wiemeyer, Jan Peters, Guilherme Maeda
In the practice of motor skills in general, errors in the execution of movements may go unnoticed when a human instructor is not available. In this case, a computer system or robotic device able to detect movement errors and propose corrections would be of great help. This paper addresses the problem of how to detect such execution errors and how to provide feedback to the human to correct his/her motor skill using a general, principled methodology based on imitation learning. The core idea is to compare the observed skill with a probabilistic model learned from expert demonstrations...
2018: Frontiers in Neurorobotics
Jaqueline Fagard, Rana Esseily, Lisa Jacquey, Kevin O'Regan, Eszter Somogyi
The aim of this article is to track the fetal origin of infants' sensorimotor behavior. We consider development as the self-organizing emergence of complex forms from spontaneously generated activity, governed by the innate capacity to detect and memorize the consequences of spontaneous activity (contingencies), and constrained by the sensory and motor maturation of the body. In support of this view, we show how observations on fetuses and also several fetal experiments suggest that the fetus's first motor activity allows it to feel the space around it and to feel its body and the consequences of its movements on its body...
2018: Frontiers in Neurorobotics
Tadahiro Taniguchi, Ryo Yoshino, Toshiaki Takano
In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object...
2018: Frontiers in Neurorobotics
Thomas Mergner, Vittorio Lippi
Posture control is indispensable for both humans and humanoid robots, which becomes especially evident when performing sensorimotor tasks such as moving on compliant terrain or interacting with the environment. Posture control is therefore targeted in recent proposals of robot benchmarking in order to advance their development. This Methods article suggests corresponding robot tests of standing balance, drawing inspirations from the human sensorimotor system and presenting examples from robot experiments. To account for a considerable technical and algorithmic diversity among robots, we focus in our tests on basic posture control mechanisms, which provide humans with an impressive postural versatility and robustness...
2018: Frontiers in Neurorobotics
Ori Ossmy, Justine E Hoch, Patrick MacAlpine, Shohan Hasan, Peter Stone, Karen E Adolph
Although both infancy and artificial intelligence (AI) researchers are interested in developing systems that produce adaptive, functional behavior, the two disciplines rarely capitalize on their complementary expertise. Here, we used soccer-playing robots to test a central question about the development of infant walking. During natural activity, infants' locomotor paths are immensely varied. They walk along curved, multi-directional paths with frequent starts and stops. Is the variability observed in spontaneous infant walking a "feature" or a "bug?" In other words, is variability beneficial for functional walking performance? To address this question, we trained soccer-playing robots on walking paths generated by infants during free play and tested them in simulated games of "RoboCup...
2018: Frontiers in Neurorobotics
Wentao Sun, Jinying Zhu, Yinlai Jiang, Hiroshi Yokoi, Qiang Huang
Estimating muscle force by surface electromyography (sEMG) is a non-invasive and flexible way to diagnose biomechanical diseases and control assistive devices such as prosthetic hands. To estimate muscle force using sEMG, a supervised method is commonly adopted. This requires simultaneous recording of sEMG signals and muscle force measured by additional devices to tune the variables involved. However, recording the muscle force of the lost limb of an amputee is challenging, and the supervised method has limitations in this regard...
2018: Frontiers in Neurorobotics
Diego Torricelli, Camilo Cortés, Nerea Lete, Álvaro Bertelsen, Jose E Gonzalez-Vargas, Antonio J Del-Ama, Iris Dimbwadyo, Juan C Moreno, Julian Florez, Jose L Pons
The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them...
2018: Frontiers in Neurorobotics
Galit Hofree, Paul Ruvolo, Audrey Reinert, Marian S Bartlett, Piotr Winkielman
Facial actions are key elements of non-verbal behavior. Perceivers' reactions to others' facial expressions often represent a match or mirroring (e.g., they smile to a smile). However, the information conveyed by an expression depends on context. Thus, when shown by an opponent, a smile conveys bad news and evokes frowning. The availability of anthropomorphic agents capable of facial actions raises the question of how people respond to such agents in social context. We explored this issue in a study where participants played a strategic game with or against a facially expressive android...
2018: Frontiers in Neurorobotics
Baojun Chen, Lorenzo Grazi, Francesco Lanotte, Nicola Vitiello, Simona Crea
Repetitive lifting of heavy loads increases the risk of back pain and even lumbar vertebral injuries to workers. Active exoskeletons can help workers lift loads by providing power assistance, and therefore reduce the moment and force applied on L5/S1 joint of human body when performing lifting tasks. However, most existing active exoskeletons for lifting assistance are unable to automatically detect user's lift movement, which limits the wide application of active exoskeletons in factories. In this paper, we propose a simple but effective lift detection strategy for exoskeleton control...
2018: Frontiers in Neurorobotics
Shaowei Yao, Yu Zhuang, Zhijun Li, Rong Song
Various rehabilitation robots have been employed to recover the motor function of stroke patients. To improve the effect of rehabilitation, robots should promote patient participation and provide compliant assistance. This paper proposes an adaptive admittance control scheme (AACS) consisting of an admittance filter, inner position controller, and electromyography (EMG)-driven musculoskeletal model (EDMM). The admittance filter generates the subject's intended motion according to the joint torque estimated by the EDMM...
2018: Frontiers in Neurorobotics
Annette Hagengruber, Hannes Höppner, Jörn Vogel
A key factor for reliable object manipulation is the tactile information provided by the skin of our hands. As this sensory information is so essential in our daily life it should also be provided during teleoperation of robotic devices or in the control of myoelectric prostheses. It is well-known that feeding back the tactile information to the user can lead to a more natural and intuitive control of robotic devices. However, in some applications it is difficult to use the hands as natural feedback channels since they may already be overloaded with other tasks or, e...
2018: Frontiers in Neurorobotics
Giulia Ballardini, Giorgio Carlini, Psiche Giannoni, Robert A Scheidt, Ilana Nisky, Maura Casadio
Many neurological diseases impair the motor and somatosensory systems. While several different technologies are used in clinical practice to assess and improve motor functions, somatosensation is evaluated subjectively with qualitative clinical scales. Treatment of somatosensory deficits has received limited attention. To bridge the gap between the assessment and training of motor vs. somatosensory abilities, we designed, developed, and tested a novel, low-cost, two-component (bimanual) mechatronic system targeting tactile somatosensation: the Tactile-STAR -a tactile stimulator and recorder...
2018: Frontiers in Neurorobotics
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