scholarly journals Human-Like Arm Motion Generation: A Review

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 102
Author(s):  
Gianpaolo Gulletta ◽  
Wolfram Erlhagen ◽  
Estela Bicho
Keyword(s):  
Human Robot Interaction ◽  
Motion Generation ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Arm Reaching ◽  
Strong Focus ◽  
Human Likeness ◽  
Human Motor ◽  
Arm Motion ◽  
Personal Robotics

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.

scholarly journals Human-Like Arm Motion Generation: A Review

2020 ◽  
Author(s):  
Gianpaolo Gulletta ◽  
Wolfram Erlhagen ◽  
Estela Bicho
Keyword(s):  
Human Robot Interaction ◽  
Motion Generation ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Arm Reaching ◽  
Strong Focus ◽  
Human Likeness ◽  
Human Motor ◽  
Arm Motion ◽  
Personal Robotics

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analysed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioural and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.

scholarly journals Human-Like Arm Motion Generation: A Review

2020 ◽  
Author(s):  
Gianpaolo Gulletta ◽  
Wolfram Erlhagen ◽  
Estela Bicho
Keyword(s):  
Human Robot Interaction ◽  
Motion Generation ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Arm Reaching ◽  
Strong Focus ◽  
Human Likeness ◽  
Human Motor ◽  
Arm Motion ◽  
Personal Robotics

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analysed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioural and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.

Humans and humanoids

2018 ◽  
Author(s):  
Giorgio Metta
Keyword(s):  
Humanoid Robot ◽  
Robot Vision ◽  
Physical Space ◽  
Humanoid Robots ◽  
Human Robot Interaction ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Humanoid Robotics ◽  
Biomimetic Robot ◽  
Compliant Actuation

This chapter outlines a number of research lines that, starting from the observation of nature, attempt to mimic human behavior in humanoid robots. Humanoid robotics is one of the most exciting proving grounds for the development of biologically inspired hardware and software—machines that try to recreate billions of years of evolution with some of the abilities and characteristics of living beings. Humanoids could be especially useful for their ability to “live” in human-populated environments, occupying the same physical space as people and using tools that have been designed for people. Natural human–robot interaction is also an important facet of humanoid research. Finally, learning and adapting from experience, the hallmark of human intelligence, may require some approximation to the human body in order to attain similar capacities to humans. This chapter focuses particularly on compliant actuation, soft robotics, biomimetic robot vision, robot touch, and brain-inspired motor control in the context of the iCub humanoid robot.

scholarly journals A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2691 ◽  
Author(s):  
Marcos Maroto-Gómez ◽  
Álvaro Castro-González ◽  
José Castillo ◽  
María Malfaz ◽  
Miguel Salichs
Keyword(s):  
Decision Making ◽  
Human Robot Interaction ◽  
Learning Mechanisms ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Detection Mechanism ◽  
Learned Behaviors ◽  
Decision Making System ◽  
Real Robot ◽  
Self Learning

Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot. Following a homeostatic approach, the ultimate goal of the robot is to keep its wellbeing as high as possible. In order to achieve this goal, our decision making system uses learning mechanisms to assess the best action to execute at any moment. Considering that the proposed system has been implemented in a real social robot, human-robot interaction is of paramount importance and the learned behaviors of the robot are oriented to foster the interactions with the user. The operation of the system is shown in a scenario where the robot Mini plays games with a user. In this context, we have included a robust user detection mechanism tailored for short distance interactions. After the learning phase, the robot has learned how to lead the user to interact with it in a natural way.

Research on Human Cognition for Biologically Inspired Developments

2017 ◽  
pp. 83-116 ◽  
Author(s):  
Marko Wehle ◽  
Alexandra Weidemann ◽  
Ivo Wilhelm Boblan
Keyword(s):  
Role Model ◽  
Cognitive Model ◽  
Human Robot Interaction ◽  
Human Cognition ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Intelligent Machines ◽  
The World ◽  
Conducting Research ◽  
Interaction Research

Robotic developments are seen as a next level in technology with intelligent machines, which automate tedious tasks and serve our needs without complaints. But nevertheless, they have to be fair and smart enough to be intuitively of use and safe to handle. But how to implement this kind of intelligence, does it need feelings and emotions, should robots perceive the world as we do as a human role model, how far should the implementation of synthetic consciousness lead and actually, what is needed for consciousness in that context? Additionally in Human-Robot-Interaction research, science mainly makes use of the tool phenomenography, which is exclusively subjective, so how to make it qualify for Artificial Intelligence? These are the heading aspects of this chapter for conducting research in the field of social robotics and suggesting a conscious and cognitive model for smart and intuitive interacting robots, guided by biomimetics.

Robot Humanization Measure/Task

2021 ◽  
Author(s):  
Nicolas Spatola ◽  
Serena Marchesi ◽  
Agnieszka Wykowska
Keyword(s):  
Individual Differences ◽  
Response Time ◽  
Response Selection ◽  
Social Categorization ◽  
Human Robot Interaction ◽  
Decision Task ◽  
Robot Interaction ◽  
Response Options ◽  
Key Factor ◽  
Human Likeness

In human-robot interaction, one key factor to predict and understand how human engage and interact with robots is how the inter-individual differences in how they perceive, consider and feel toward robots. Building on the theories of social categorization and dehumanization we aimed to develop a new tool assessing the perceived conceptual distance between humans and robots when observing robotic actions. In three studies we developed and validated the structure of a task aiming at evaluating to what extent individuals humanize robots. In this task participants were required to judge the human-likeness of robotic actions on a robot/human continuum represented by silhouettes. In a fourth study, we adapted this new tool to a decision task (with two response options robot/human) in which response time and response selection are used to infer the robot humanization bias of participants. Results showed reliable psychometric structure of the present measure in both questionnaire and decision task format. We further discuss how social categorization bias in HRI may be relevant to better predict attitudes toward robots.

EXTENDING FEYNMAN'S FORMALISMS FOR MODELING HUMAN JOINT ACTION COORDINATION

2009 ◽  
Vol 02 (01) ◽  
pp. 1-7 ◽  
Author(s):  
VLADIMIR G. IVANCEVIC ◽  
EUGENE V. AIDMAN ◽  
LEONG YEN
Keyword(s):  
Joint Action ◽  
Human Action ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Infinite Dimensional ◽  
Proposed Model ◽  
Potential Applications ◽  
Individual Actor ◽  
Human Motor ◽  
Interaction Research

The recently developed Life-Space-Foam approach to goal-directed human action deals with individual actor dynamics. This paper applies the model to characterize the dynamics of co-action by two or more actors. This dynamics is modelled by (i) a two-term joint action (including cognitive/motivatonal potential and kinetic energy), and (ii) its associated adaptive path integral, representing an infinite-dimensional neural network. Its feedback adaptation loop has been derived from Bernstein's concepts of sensory corrections loop in human motor control and Brooks' subsumption architectures in robotics. Potential applications of the proposed model in human-robot interaction research are discussed.

scholarly journals The Human Takes It All: Humanlike Synthesized Voices Are Perceived as Less Eerie and More Likable. Evidence From a Subjective Ratings Study

2020 ◽  
Vol 14 ◽  
Author(s):  
Katharina Kühne ◽  
Martin H. Fischer ◽  
Yuefang Zhou
Keyword(s):  
Qualitative Content Analysis ◽  
Human Perception ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Diagnostic Features ◽  
Human Voice ◽  
Human Likeness ◽  
Speech Features ◽  
The Voice

Background: The increasing involvement of social robots in human lives raises the question as to how humans perceive social robots. Little is known about human perception of synthesized voices.Aim: To investigate which synthesized voice parameters predict the speaker's eeriness and voice likability; to determine if individual listener characteristics (e.g., personality, attitude toward robots, age) influence synthesized voice evaluations; and to explore which paralinguistic features subjectively distinguish humans from robots/artificial agents.Methods: 95 adults (62 females) listened to randomly presented audio-clips of three categories: synthesized (Watson, IBM), humanoid (robot Sophia, Hanson Robotics), and human voices (five clips/category). Voices were rated on intelligibility, prosody, trustworthiness, confidence, enthusiasm, pleasantness, human-likeness, likability, and naturalness. Speakers were rated on appeal, credibility, human-likeness, and eeriness. Participants' personality traits, attitudes to robots, and demographics were obtained.Results: The human voice and human speaker characteristics received reliably higher scores on all dimensions except for eeriness. Synthesized voice ratings were positively related to participants' agreeableness and neuroticism. Females rated synthesized voices more positively on most dimensions. Surprisingly, interest in social robots and attitudes toward robots played almost no role in voice evaluation. Contrary to the expectations of an uncanny valley, when the ratings of human-likeness for both the voice and the speaker characteristics were higher, they seemed less eerie to the participants. Moreover, when the speaker's voice was more humanlike, it was more liked by the participants. This latter point was only applicable to one of the synthesized voices. Finally, pleasantness and trustworthiness of the synthesized voice predicted the likability of the speaker's voice. Qualitative content analysis identified intonation, sound, emotion, and imageability/embodiment as diagnostic features.Discussion: Humans clearly prefer human voices, but manipulating diagnostic speech features might increase acceptance of synthesized voices and thereby support human-robot interaction. There is limited evidence that human-likeness of a voice is negatively linked to the perceived eeriness of the speaker.

scholarly journals Dynamic Input Deep Learning Control of Artificial Avatars in a Multi-Agent Joint Motor Task

2021 ◽  
Vol 8 ◽  
Author(s):  
Maria Lombardi ◽  
Davide Liuzza ◽  
Mario di Bernardo
Keyword(s):  
Motor Coordination ◽  
Motor Task ◽  
Human Robot Interaction ◽  
Artificial Agents ◽  
Artificial Agent ◽  
Common Goal ◽  
Robot Interaction ◽  
Joint Task ◽  
Multi Agent ◽  
Human Motor

In many real-word scenarios, humans and robots are required to coordinate their movements in joint tasks to fulfil a common goal. While several examples regarding dyadic human robot interaction exist in the current literature, multi-agent scenarios in which one or more artificial agents need to interact with many humans are still seldom investigated. In this paper we address the problem of synthesizing an autonomous artificial agent to perform a paradigmatic oscillatory joint task in human ensembles while exhibiting some desired human kinematic features. We propose an architecture based on deep reinforcement learning which is flexible enough to make the artificial agent interact with human groups of different sizes. As a paradigmatic coordination task we consider a multi-agent version of the mirror game, an oscillatory motor task largely used in the literature to study human motor coordination.

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