scholarly journals A MLP-Hedge-Algebras Admittance Controller for Physical Human–Robot Interaction

2021 ◽  
Vol 11 (12) ◽  
pp. 5459
Author(s):  
Nguyen-Van Toan ◽  
Phan-Bui Khoi ◽  
Soo-Yeong Yi
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Multilayer Perceptron ◽  
Human Robot Interaction ◽  
Input Output ◽  
Robot Interaction ◽  
Inference Mechanism ◽  
Safety Issues ◽  
First Time

Recently, the identification of inertia and damping matrices (IIDM) and safety issues, as well as natural cooperation, are interestingly considered to enhance the quality of the physical human–robot interaction (pHRI). To cover all of these issues, advanced admittance controllers, such as those based on fuzzy logic or hedge algebras, have been formulated and successfully applied in several industrial problems. However, the inference mechanism of those kinds of controllers causes the discreteness of the super surface describing the input–output relationship in the Cartesian coordinates. As a consequence, the quality of the safe-natural cooperation between humans and robots is negatively affected. This paper presents an alternative admittance controller for pHRI by using a combination of hedge algebras and multilayer perceptron neural network (MLP), whose purpose is to create a more accurate inference mechanism for the admittance controller. To our best knowledge, this is the first time that such a neural network is considered for the inference mechanism of hedge algebras and also the first time that such an admittance controller is used for pHRI. The proposed admittance controller is verified on a teaching task using a 6-DOF manipulator. Experimental results have shown that the proposed method provides better cooperation compared with previous methods.

Human–robot collisions detection for safe human–robot interaction using one multi-input–output neural network

2019 ◽  
Vol 24 (9) ◽  
pp. 6687-6719 ◽  
Author(s):  
Abdel-Nasser Sharkawy ◽  
Panagiotis N. Koustoumpardis ◽  
Nikos Aspragathos
Keyword(s):  
Neural Network ◽  
Human Robot Interaction ◽  
Input Output ◽  
Robot Interaction

scholarly journals Active stability observer using artificial neural network for intuitive physical human–robot interaction

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141772732 ◽  
Author(s):  
Mohamed Amir Sassi ◽  
Martin J-D Otis ◽  
Alexandre Campeau-Lecours
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Analysis ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Human Robot Interaction ◽  
Sensor Signal ◽  
Robot Interaction ◽  
Artificial Neural ◽  
Real Time Simulations

Physical human–robot interaction may present an obstacle to transparency and operations’ intuitiveness. This barrier could occur due to the vibrations caused by a stiff environment interacting with the robotic mechanisms. In this regard, this article aims to deal with the aforementioned issues while using an observer and an adaptive gain controller. The adaptation of the gain loop should be performed in all circumstances in order to maintain operators’ safety and operations’ intuitiveness. Hence, two approaches for detecting and then reducing vibrations will be introduced in this study as follows: (1) a statistical analysis of a sensor signal (force and velocity) and (2) a multilayer perceptron artificial neural network capable of compensating the first approach for ensuring vibrations identification in real time. Simulations and experimental results are then conducted and compared in order to evaluate the validity of the suggested approaches in minimizing vibrations.

Shall I trust you? From child human-robot interaction to trusting relationships

2019 ◽  
Author(s):  
Cinzia Di Dio ◽  
Federico Manzi ◽  
Giulia Peretti ◽  
Angelo Cangelosi ◽  
Paul L. Harris ◽  
...  
Keyword(s):  
Behavioral Pattern ◽  
Human Robot Interaction ◽  
School Age Children ◽  
Social Relevance ◽  
Robot Interaction ◽  
Cognitive Correlates ◽  
The Social ◽  
The Relationship

Studying trust within human-robot interaction is of great importance given the social relevance of robotic agents in a variety of contexts. We investigated the acquisition, loss and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in-vivo. The relationship between trust and the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. No differences were found in children’s trust in the play-partner as a function of agency (human or robot). Nevertheless, 3-years-olds showed a trend toward trusting the human more than the robot, while 7-years-olds displayed the reverse behavioral pattern, thus highlighting the developing interplay between affective and cognitive correlates of trust.

Control of Autonomous Robots Using the Principles of Neuromodulation

2013 ◽  
Author(s):  
Akimul Prince ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Autonomous Robots ◽  
Neuronal Model ◽  
Autonomous Robot ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Control Approach ◽  
The Neural Network ◽  
Simple Neural Network ◽  
Interesting Behavior

The paper presents a control approach based on vertebrate neuromodulation and its implementation on an autonomous robot platform. A simple neural network is used to model the neuromodulatory function for generating context based behavioral responses to sensory signals. The neural network incorporates three types of neurons — cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behavior. The implementation of the neuronal model on a relatively simple autonomous robot illustrates its interesting behavior adapting to changes in the environment. The integration of neuromodulation based robots in the study of human-robot interaction would be worth considering in future.

Gestures Identification for Myoelectric Based Control of the Robotic Arm

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Adhau P ◽  
◽  
Kadwane S. G ◽  
Shital Telrandhe ◽  
Rajguru V. S ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Human Robot Interaction ◽  
Robotic Arm ◽  
Human Hand ◽  
Robot Interaction ◽  
The Neural Network

Human robot interaction have been ever the topic of research to research scholars owing to its importance to help humanity. Robust human interacting robot where commands from Electromyogram (EMG) signals is recently being investigated. This article involves study of motions a system that allows signals recorded directly from a human body and thereafter can be used for control of a small robotic arm. The various gestures are recognized by placing the electrodes or sensors on the human hand. These gestures are then identified by using neural network. The neural network will thus train the signals. The offline control of the arm is done by controlling the motors of the robotic arm.

scholarly journals HUMAN-ROBOT INTERACTION: LANGUAGE ACQUISITION WITH NEURAL NETWORK

2018 ◽  
Vol 11 (1) ◽  
pp. 75-84
Author(s):  
Alvin Rindra Fazrie
Keyword(s):  
Neural Network ◽  
Language Acquisition ◽  
Language Processing ◽  
Language Game ◽  
Human Robot Interaction ◽  
Communication Process ◽  
Robot Interaction ◽  
Echo State Networks ◽  
Stochastic Learning ◽  
Communicative Efficiency

ABSTRACT The paper gives an overview about the process between two language processing methods towards Human-robot interaction. In this paper, Echo State Networks and Stochastic-learning grammar are explored in order to get an idea about generating human’s natural language and the possibilities of integrating these methods to make the communication process between robot to robot or robot to human to be more natural in dialogic syntactic language game. The methods integration could give several benefits such as improving the communicative efficiency and producing the more natural communication sentence.   ABSTRAK Tulisan ini memberikan penjabaran mengenai dua metode pemrosesan bahasa alami pada interaksi Manusia dan Robot. Echo State Networks adalah salah satu arsitektur dari Jaringan Syaraf Tiruan yang berdasarkan prinsip Supervised Learning untuk Recurrent Neural Network, dieksplorasi bersama Stochastic-learning Grammar yaitu salah satu framework tata bahasa dengan konsep probabilistik yang bertujuan untuk mendapatkan ide bagaimana proses bahasa alami dari manusia dan kemungkinannya mengintegrasikan dua metode tersebut untuk membuat proses komunikasi antara robot dengan robot atau robot dengan manusia menjadi lebih natural dalam dialogic syntactic language game. Metode integrasi dapat memberikan beberapa keuntungan seperti meningkatkan komunikasi yang efisien dan dapat membuat konstruksi kalimat saat komunikasi menjadi lebih natural. How To Cite : Fazrie, A.R. (2018). HUMAN-ROBOT INTERACTION: LANGUAGE ACQUISITION WITH NEURAL NETWORK. Jurnal Teknik Informatika, 11(1), 75-84.  doi 10.15408/jti.v11i1.6093 Permalink/DOI: http://dx.doi.org/10.15408/jti.v11i1.6093 

Analysis of Pedagogy of Teacher's Capability to Transform Knowledge Into Practice Using Fuzzy Logic

2019 ◽  
Vol 8 (2) ◽  
pp. 16-33
Author(s):  
Jagmohan Mago ◽  
Dinesh Kumar
Keyword(s):  
Fuzzy Logic ◽  
Current Literature ◽  
Fuzzy Inference ◽  
Inference Mechanism ◽  
Common Practices ◽  
Consistent Method ◽  
Fuzzy Inference Mechanism ◽  
Personal Behavior

Current literature and common practices suggest that there is no consistent method available to analyze the performance of teachers. Due to its inherent vagueness and uncertainty, this article analyzes the effectiveness of a teacher depending upon various factors using fuzzy logic. It explains various parameters influencing professional, interpersonal and personal behavior of teachers. Secondly, a fuzzy inference mechanism is developed to decide the possible quality of teachers. The article concludes by observing that the proposed fuzzy logic based system is consistent with that judged by the experts and can be used to predict the possible quality of teachers.

scholarly journals State-Transition Modeling of Human–Robot Interaction for Easy Crowdsourced Robot Control

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6529
Author(s):  
Masaya Iwasaki ◽  
Mizuki Ikeda ◽  
Tatsuyuki Kawamura ◽  
Hideyuki Nakanishi
Keyword(s):  
User Interface ◽  
Social Presence ◽  
Robot Control ◽  
State Transition ◽  
Human Robot Interaction ◽  
Sales Promotion ◽  
Robot Interaction ◽  
Sensor Information ◽  
Transition Modeling ◽  
First Time

Robotic salespeople are often ignored by people due to their weak social presence, and thus have difficulty facilitating sales autonomously. However, for robots that are remotely controlled by humans, there is a need for experienced and trained operators. In this paper, we suggest crowdsourcing to allow general users on the internet to operate a robot remotely and facilitate customers’ purchasing activities while flexibly responding to various situations through a user interface. To implement this system, we examined how our developed remote interface can improve a robot’s social presence while being controlled by a human operator, including first-time users. Therefore, we investigated the typical flow of a customer–robot interaction that was effective for sales promotion, and modeled it as a state transition with automatic functions by accessing the robot’s sensor information. Furthermore, we created a user interface based on the model and examined whether it was effective in a real environment. Finally, we conducted experiments to examine whether the user interface could be operated by an amateur user and enhance the robot’s social presence. The results revealed that our model was able to improve the robot’s social presence and facilitate customers’ purchasing activity even when the operator was a first-time user.

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