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Frontiers in Neurorobotics

Keum-Shik Hong, Amad Zafar
A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity...
2018: Frontiers in Neurorobotics
Rania Rayyes, Daniel Kubus, Jochen Steil
Learning (inverse) kinematics and dynamics models of dexterous robots for the entire action or observation space is challenging and costly. Sampling the entire space is usually intractable in terms of time, tear, and wear. We propose an efficient approach to learn inverse statics models-primarily for gravity compensation-by exploring only a small part of the configuration space and exploiting the symmetry properties of the inverse statics mapping. In particular, there exist symmetric configurations that require the same absolute motor torques to be maintained...
2018: Frontiers in Neurorobotics
Lakshitha P Wijesinghe, Jochen Triesch, Bertram E Shi
We propose a biologically inspired model that enables a humanoid robot to learn how to track its end effector by integrating visual and proprioceptive cues as it interacts with the environment. A key novel feature of this model is the incorporation of sensorimotor prediction, where the robot predicts the sensory consequences of its current body motion as measured by proprioceptive feedback. The robot develops the ability to perform smooth pursuit-like eye movements to track its hand, both in the presence and absence of visual input, and to track exteroceptive visual motions...
2018: Frontiers in Neurorobotics
Carlotta Mummolo, William Z Peng, Shlok Agarwal, Robert Griffin, Peter D Neuhaus, Joo H Kim
The assessment of the risk of falling during robot-assisted locomotion is critical for gait control and operator safety, but has not yet been addressed through a systematic and quantitative approach. In this study, the balance stability of Mina v2, a recently developed powered lower-limb robotic exoskeleton, is evaluated using an algorithmic framework based on center of mass (COM)- and joint-space dynamics. The equivalent mechanical model of the combined human-exoskeleton system in the sagittal plane is established and used for balance stability analysis...
2018: Frontiers in Neurorobotics
Francesco Scotto di Luzio, Davide Simonetti, Francesca Cordella, Sandra Miccinilli, Silvia Sterzi, Francesco Draicchio, Loredana Zollo
The design of patient-tailored rehabilitative protocols represents one of the crucial factors that influence motor recovery mechanisms, such as neuroplasticity. This approach, including the patient in the control loop and characterized by a control strategy adaptable to the user's requirements, is expected to significantly improve functional recovery in robot-aided rehabilitation. In this paper, a novel 3D bio-cooperative robotic platform is developed. A new arm-weight support system is included into an operational robotic platform for 3D upper limb robot-aided rehabilitation...
2018: Frontiers in Neurorobotics
Sherif Abdelfattah, Kathryn Kasmarik, Jiankun Hu
Many real-world decision-making problems involve multiple conflicting objectives that can not be optimized simultaneously without a compromise. Such problems are known as multi-objective Markov decision processes and they constitute a significant challenge for conventional single-objective reinforcement learning methods, especially when an optimal compromise cannot be determined beforehand. Multi-objective reinforcement learning methods address this challenge by finding an optimal coverage set of non-dominated policies that can satisfy any user's preference in solving the problem...
2018: Frontiers in Neurorobotics
Paresh Dhakan, Kathryn Merrick, Iñaki Rañó, Nazmul Siddique
In reinforcement learning, reward is used to guide the learning process. The reward is often designed to be task-dependent, and it may require significant domain knowledge to design a good reward function. This paper proposes general reward functions for maintenance, approach, avoidance, and achievement goal types. These reward functions exploit the inherent property of each type of goal and are thus task-independent. We also propose metrics to measure an agent's performance for learning each type of goal. We evaluate the intrinsic reward functions in a framework that can autonomously generate goals and learn solutions to those goals using a standard reinforcement learning algorithm...
2018: Frontiers in Neurorobotics
Tianrui Liu, Tania Stathaki
Convolutional neural networks (CNN) have enabled significant improvements in pedestrian detection owing to the strong representation ability of the CNN features. However, it is generally difficult to reduce false positives on hard negative samples such as tree leaves, traffic lights, poles, etc. Some of these hard negatives can be removed by making use of high level semantic vision cues. In this paper, we propose a region-based CNN method which makes use of semantic cues for better pedestrian detection. Our method extends the Faster R-CNN detection framework by adding a branch of network for semantic image segmentation...
2018: Frontiers in Neurorobotics
Miguel Aguilera, Manuel G Bedia
The activity of many biological and cognitive systems is not poised deep within a specific regime of activity. Instead, they operate near points of critical behavior located at the boundary between different phases. Certain authors link some of the properties of criticality with the ability of living systems to generate autonomous or intrinsically generated behavior. However, these claims remain highly speculative. In this paper, we intend to explore the connection between criticality and autonomous behavior through conceptual models that show how embodied agents may adapt themselves toward critical points...
2018: Frontiers in Neurorobotics
Eiji Uchibe
This paper proposes Cooperative and competitive Reinforcement And Imitation Learning (CRAIL) for selecting an appropriate policy from a set of multiple heterogeneous modules and training all of them in parallel. Each learning module has its own network architecture and improves the policy based on an off-policy reinforcement learning algorithm and behavior cloning from samples collected by a behavior policy that is constructed by a combination of all the policies. Since the mixing weights are determined by the performance of the module, a better policy is automatically selected based on the learning progress...
2018: Frontiers in Neurorobotics
Stephane Doncieux, David Filliat, Natalia Díaz-Rodríguez, Timothy Hospedales, Richard Duro, Alexandre Coninx, Diederik M Roijers, Benoît Girard, Nicolas Perrin, Olivier Sigaud
Reinforcement learning (RL) aims at building a policy that maximizes a task-related reward within a given domain. When the domain is known, i.e., when its states, actions and reward are defined, Markov Decision Processes (MDPs) provide a convenient theoretical framework to formalize RL. But in an open-ended learning process, an agent or robot must solve an unbounded sequence of tasks that are not known in advance and the corresponding MDPs cannot be built at design time. This defines the main challenges of open-ended learning: how can the agent learn how to behave appropriately when the adequate states, actions and rewards representations are not given? In this paper, we propose a conceptual framework to address this question...
2018: Frontiers in Neurorobotics
Alessandro Scano, Andrea Chiavenna, Lorenzo Molinari Tosatti, Henning Müller, Manfredo Atzori
Background: Kinematic and muscle patterns underlying hand grasps have been widely investigated in the literature. However, the identification of a reduced set of motor modules, generalizing across subjects and grasps, may be valuable for increasing the knowledge of hand motor control, and provide methods to be exploited in prosthesis control and hand rehabilitation. Methods: Motor muscle synergies were extracted from a publicly available database including 28 subjects, executing 20 hand grasps selected for daily-life activities...
2018: Frontiers in Neurorobotics
Benjamin Cohen-Lhyver, Sylvain Argentieri, Bruno Gas
Over the last 20 years, a significant part of the research in exploratory robotics partially switches from looking for the most efficient way of exploring an unknown environment to finding what could motivate a robot to autonomously explore it. Moreover, a growing literature focuses not only on the topological description of a space (dimensions, obstacles, usable paths, etc.) but rather on more semantic components, such as multimodal objects present in it. In the search of designing robots that behave autonomously by embedding life-long learning abilities, the inclusion of mechanisms of attention is of importance...
2018: Frontiers in Neurorobotics
Iris Kyranou, Sethu Vijayakumar, Mustafa Suphi Erden
Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance...
2018: Frontiers in Neurorobotics
Feifei Zhao, Yi Zeng, Bo Xu
Decision-making is a crucial cognitive function for various animal species surviving in nature, and it is also a fundamental ability for intelligent agents. To make a step forward in the understanding of the computational mechanism of human-like decision-making, this paper proposes a brain-inspired decision-making spiking neural network (BDM-SNN) and applies it to decision-making tasks on intelligent agents. This paper makes the following contributions: (1) A spiking neural network (SNN) is used to model human decision-making neural circuit from both connectome and functional perspectives...
2018: Frontiers in Neurorobotics
Christopher Reardon, Hao Zhang, Jonathan Fink
A fundamental problem in creating successful shared autonomy systems is enabling efficient specification of the problem for which an autonomous system can generate a solution. We present a general paradigm, Interactive Shared Solution Shaping (IS3), broadly applied to shared autonomous systems where a human can use their domain knowledge to interactively provide feedback during the autonomous planning process. We hypothesize that this interaction process can be optimized so that with minimal interaction, near-optimal solutions can be achieved...
2018: Frontiers in Neurorobotics
Erick J Chastain
As part of the extended evolutionary synthesis, there has recently been a new emphasis on the effects of biological development on genetic inheritance and variation. The exciting new directions taken by those in the community have by a pre-history filled with related ideas that were never given a rigorous foundation or combined coherently. Part of the historical background of the extended synthesis is the work of James Mark Baldwin on his so-called "Baldwin Effect." Many variant re-interpretations of his work obscure the original meaning of the Baldwin Effect...
2018: 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
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