Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems

Work ◽  
2021 ◽  
pp. 1-10
Author(s):  
Hai Tao ◽  
MdArafatur Rahman ◽  
Wang Jing ◽  
Yafeng Li ◽  
Jing Li ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Research Field ◽  
Human Robot Interaction ◽  
Robotic Systems ◽  
Multiple Sources ◽  
Robot Interaction ◽  
Prime Concern ◽  
Interaction Modeling ◽  
Error Classification ◽  
Two Phases

BACKGROUND: Human-Robot Interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users. OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection. RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs. CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.

Robotic Hardware and Software Integration for Changing Human Intentions

2012 ◽  
pp. 380-406 ◽  
Author(s):  
Akif Durdu ◽  
Ismet Erkmen ◽  
Aydan M. Erkmen ◽  
Alper Yilmaz
Keyword(s):  
Video Processing ◽  
Spatial Information ◽  
Human Robot Interaction ◽  
Phase System ◽  
Robot Interaction ◽  
Two Phase ◽  
Video Frames ◽  
Human Intention ◽  
Two Phases ◽  
Intention Estimation

Estimating and reshaping human intentions are among the most significant topics of research in the field of human-robot interaction. This chapter provides an overview of intention estimation literature on human-robot interaction, and introduces an approach on how robots can voluntarily reshape estimated intentions. The reshaping of the human intention is achieved by the robots moving in certain directions that have been a priori observed from the interactions of humans with the objects in the scene. Being among the only few studies on intention reshaping, the authors of this chapter exploit spatial information by learning a Hidden Markov Model (HMM) of motion, which is tailored for intelligent robotic interaction. The algorithmic design consists of two phases. At first, the approach detects and tracks human to estimate the current intention. Later, this information is used by autonomous robots that interact with detected human to change the estimated intention. In the tracking and intention estimation phase, postures and locations of the human are monitored by applying low-level video processing methods. In the latter phase, learned HMM models are used to reshape the estimated human intention. This two-phase system is tested on video frames taken from a real human-robot environment. The results obtained using the proposed approach shows promising performance in reshaping of detected intentions.

scholarly journals Robotic Hardware and Software Integration for Changing Human Intentions

Robotics ◽  
2013 ◽  
pp. 1381-1406
Author(s):  
Akif Durdu ◽  
Ismet Erkmen ◽  
Aydan M. Erkmen ◽  
Alper Yilmaz
Keyword(s):  
Video Processing ◽  
Spatial Information ◽  
Human Robot Interaction ◽  
Phase System ◽  
Robot Interaction ◽  
Two Phase ◽  
Video Frames ◽  
Human Intention ◽  
Two Phases ◽  
Intention Estimation

Estimating and reshaping human intentions are among the most significant topics of research in the field of human-robot interaction. This chapter provides an overview of intention estimation literature on human-robot interaction, and introduces an approach on how robots can voluntarily reshape estimated intentions. The reshaping of the human intention is achieved by the robots moving in certain directions that have been a priori observed from the interactions of humans with the objects in the scene. Being among the only few studies on intention reshaping, the authors of this chapter exploit spatial information by learning a Hidden Markov Model (HMM) of motion, which is tailored for intelligent robotic interaction. The algorithmic design consists of two phases. At first, the approach detects and tracks human to estimate the current intention. Later, this information is used by autonomous robots that interact with detected human to change the estimated intention. In the tracking and intention estimation phase, postures and locations of the human are monitored by applying low-level video processing methods. In the latter phase, learned HMM models are used to reshape the estimated human intention. This two-phase system is tested on video frames taken from a real human-robot environment. The results obtained using the proposed approach shows promising performance in reshaping of detected intentions.

Speaker Localization Based on Phase Detection of Vowel Fundamental Frequencies

2012 ◽  
Vol 433-440 ◽  
pp. 6490-6496
Author(s):  
Jin Shan Gao ◽  
Shi Jie Wang
Keyword(s):  
Microphone Array ◽  
Interaural Time Difference ◽  
Research Field ◽  
Human Robot Interaction ◽  
Phase Detection ◽  
Speech Signal Processing ◽  
Robot Interaction ◽  
Fundamental Frequencies ◽  
Voice Signal ◽  
Speaker Localization

Research on human-robot interaction has recently been getting an increasing amount of attention. In the research field of human-robot interaction, speech signal processing in particular is the source of much interest. In this paper, we present experiment of speaker localization using a microphone array and an ITD (Interaural Time Difference) method which finds the sound source by phase shift of two signals. Band pass filters are designed to get vowel fundamental frequencies. Phase sensitive detectors are applied to measure the phase differences of voice signal of different microphones. All circuits are constructed by FPAA (field programmable analog array). The proposed system can output speaker location in real-time with low power.

scholarly journals Human-robot Interaction in Collaborative Robotic Systems

2021 ◽  
Vol 24 (4) ◽  
pp. 180-199
Author(s):  
R. R. Galin ◽  
V. V. Serebrennyj ◽  
G. K. Tevyashov ◽  
A. A. Shiroky
Keyword(s):  
Human Robot Interaction ◽  
Robotic Systems ◽  
Organizational Systems ◽  
Optimal Distribution ◽  
Systems Management ◽  
Robot Interaction ◽  
Organizational System ◽  
Collaborative Robotics ◽  
Collaborative Robots ◽  
Formal Problem

Purpose or research is to find solvable tasks for increasing the effectiveness of collaborative interaction between people and robots in ergatic robotic systems, or, in other words, in collaborative robotic systems. Methods. A comprehensive analysis of works published in highly rated peer-reviewed open-access scientific publications was carried out to achieve this goal. Main terms and concepts of collaborative robotics are described in § 1 and their current understanding in the research community is also described. The structure of workspaces in interaction zone of a person and robot is described. The criteria for assigning robot to the class of collaborative ones are also described. The criteria for safe interaction of a person and robot in a single workspace is described in § 2. Various grounds for classifying human-robot interactions in collaborative RTAs are described in § 3. Results. A significant part of published works about collaborative robotics is devoted to the organization of safe man and robot interaction. Less attention is paid to the effectiveness improvement of such interaction. An up-to-date task in the problem of efficiency improvement of collaborative robotic systems is the identification of tasks that have already been solved in other areas - in particular, in the field of organizational systems management. The possibility of using the term "team" for collaborative robots in a collaborative PTC is stated in § 4. A formal problem setting of optimal distribution in teamwork of collaborative robots, similar to the problem of heterogeneous team formation in the theory of organizational systems management is proposed in § 5. Conclusions. Proposed task setting of optimal distribution of works in collaborative robots’ team shows possibility of using results obtained in group of mathematical models of commands formation and functioning for control of collaborative robotic systems in order to increase efficiency of people and robots interaction. It is prospectively to continue the search for adapting models and governance mechanisms to the theory of organizational system management and integrated activities methodology.

scholarly journals Force-Sensing Tensegrity for Investigating Physical Human-Robot Interaction in Compliant Robotic Systems

2021 ◽  
Author(s):  
Andrew R. Barkan ◽  
Akhil Padmanabha ◽  
Sala R. Tiemann ◽  
Albert Lee ◽  
Matthew P. Kanter ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Robotic Systems ◽  
Force Sensing ◽  
Robot Interaction

Nonverbal Communication Based on Instructed Learning for Socially Embedded Robot Partners

2019 ◽  
Vol 23 (3) ◽  
pp. 584-591 ◽  
Author(s):  
Ryosuke Tanaka ◽  
◽  
Jinseok Woo ◽  
Naoyuki Kubota
Keyword(s):  
Nonverbal Communication ◽  
Daily Life ◽  
Interaction Mechanism ◽  
Essential Elements ◽  
Research Field ◽  
Human Robot Interaction ◽  
Important Research ◽  
Robot Interaction ◽  
Human Gestures ◽  
Series Of Experiments

The research and development of robot partners have been actively conducted to support human daily life. Human-robot interaction is one of the important research field, in which verbal and nonverbal communication are essential elements for improving the interactions between humans and robots. Thus, the purpose of this research was to establish a method to adapt a human-robot interaction mechanism for robot partners to various situations. In the proposed system, the robot needs to analyze the gestures of humans to interact with them. Humans have the ability to interact according to dynamically changing environmental conditions. Therefore, when robots interact with a human, it is necessary for robots to interact appropriately by correctly judging the situation according to human gestures to carry out natural human-robot interaction. In this paper, we propose a constructive methodology on a system that enables nonverbal communication elements for human-robot interaction. The proposed method was validated through a series of experiments.

Sign in / Sign up

Export Citation Format

Share Document