Effective learning system techniques for human–robot interaction in service environment

2007 ◽  
Vol 20 (5) ◽  
pp. 439-456 ◽  
Author(s):  
Z. Zenn Bien ◽  
Hyong-Euk Lee
Keyword(s):  
Human Robot Interaction ◽  
Learning System ◽  
Robot Interaction ◽  
Effective Learning ◽  
Service Environment

How many words can my robot learn?

2007 ◽  
Vol 8 (1) ◽  
pp. 53-81 ◽  
Author(s):  
Luís Seabra Lopes ◽  
Aneesh Chauhan
Keyword(s):  
Word Learning ◽  
Real World ◽  
Scale Up ◽  
Human Robot Interaction ◽  
Learning System ◽  
Evaluation Methodology ◽  
Robot Interaction ◽  
Instructor Feedback ◽  
Discriminatory Capacity ◽  
Robotic Agent

This paper addresses word learning for human–robot interaction. The focus is on making a robotic agent aware of its surroundings, by having it learn the names of the objects it can find. The human user, acting as instructor, can help the robotic agent ground the words used to refer to those objects. A lifelong learning system, based on one-class learning, was developed (OCLL). This system is incremental and evolves with the presentation of any new word, which acts as a class to the robot, relying on instructor feedback. A novel experimental evaluation methodology, that takes into account the open-ended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. The results indicate that the robot’s representations are capable of incrementally evolving by correcting class descriptions, based on instructor feedback to classification results. In successive experiments, it was possible for the robot to learn between 6 and 12 names of real-world office objects. Although these results are comparable to those obtained by other authors, there is a need to scale-up. The limitations of the method are discussed and potential directions for improvement are pointed out.

Robot feedback shapes the tutor’s presentation

2013 ◽  
Vol 14 (2) ◽  
pp. 268-296 ◽  
Author(s):  
Karola Pitsch ◽  
Anna-Lisa Vollmer ◽  
Manuel Mühlig
Keyword(s):  
Social Learning ◽  
Conversation Analysis ◽  
Paradigm Shift ◽  
Adult Child ◽  
Human Robot Interaction ◽  
Learning System ◽  
Robot Interaction ◽  
Child Interaction ◽  
Feedback Strategies ◽  
Online Feedback

The paper investigates the effects of a humanoid robot’s online feedback during a tutoring situation in which a human demonstrates how to make a frog jump across a table. Motivated by micro-analytic studies of adult-child-interaction, we investigated whether tutors react to a robot’s gaze strategies while they are presenting an action. And if so, how they would adapt to them. Analysis reveals that tutors adjust typical “motionese” parameters (pauses, speed, and height of motion). We argue that a robot – when using adequate online feedback strategies – has at its disposal an important resource with which it could proactively shape the tutor’s presentation and help generate the input from which it would benefit most. These results advance our understanding of robotic “Social Learning” in that they suggest a paradigm shift towards considering human and robot as one interational learning system. Keywords: human-robot-interaction; feedback; adaptation; multimodality; gaze; conversation analysis; social learning; pro-active robot conduct

scholarly journals Analysis of Physical Human–Robot Interaction for Motor Learning with Physical Help

2008 ◽  
Vol 5 (4) ◽  
pp. 213-223 ◽  
Author(s):  
Shuhei Ikemoto ◽  
Takashi Minato ◽  
Hiroshi Ishiguro
Keyword(s):  
Control System ◽  
Motor Learning ◽  
Human Subject ◽  
Humanoid Robot ◽  
Human Robot Interaction ◽  
Learning System ◽  
Robot Interaction ◽  
Physical Interactions ◽  
The Difference ◽  
Important Extension

In this paper, we investigate physical human–robot interaction (PHRI) as an important extension of traditional HRI research. The aim of this research is to develop a motor learning system that uses physical help from a human helper. We first propose a new control system that takes advantage of inherent joint flexibility. This control system is applied on a new humanoid robot called CB2. In order to clarify the difference between successful and unsuccessful interaction, we conduct an experiment where a human subject has to help the CB2robot in its rising-up motion. We then develop a new measure that demonstrates the difference between smooth and non-smooth physical interactions. An analysis of the experiment’s data, based on the introduced measure, shows significant differences between experts and beginners in human–robot interaction.

scholarly journals Robot Anticipation Learning System for Ball Catching

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 113
Author(s):  
Diogo Carneiro ◽  
Filipe Silva ◽  
Petia Georgieva
Keyword(s):  
Robot Control ◽  
Flight Time ◽  
Human Robot Interaction ◽  
Learning System ◽  
Robot Interaction ◽  
Trajectory Estimation ◽  
Hand Motion ◽  
Ball Flight ◽  
Ball Motion ◽  
Baseline Approach

Catching flying objects is a challenging task in human–robot interaction. Traditional techniques predict the intersection position and time using the information obtained during the free-flying ball motion. A common pain point in these systems is the short ball flight time and uncertainties in the ball’s trajectory estimation. In this paper, we present the Robot Anticipation Learning System (RALS) that accounts for the information obtained from observation of the thrower’s hand motion before the ball is released. RALS takes extra time for the robot to start moving in the direction of the target before the opponent finishes throwing. To the best of our knowledge, this is the first robot control system for ball-catching with anticipation skills. Our results show that the information fused from both throwing and flying motions improves the ball-catching rate by up to 20% compared to the baseline approach, with the predictions relying only on the information acquired during the flight phase.

A Comparison of Bystander and Team Member Distance in Human--Robot Interaction

2014 ◽  
Author(s):  
Mitchell S. Dunfee ◽  
Tracy Sanders ◽  
Peter A. Hancock
Keyword(s):  
Team Member ◽  
Human Robot Interaction ◽  
Robot Interaction

Modeling Human-Robot Interaction with Petri-Nets

2009 ◽  
Author(s):  
Rosemarie Yagoda ◽  
Michael D. Coovert
Keyword(s):  
Petri Nets ◽  
Human Robot Interaction ◽  
Robot Interaction

Workload in Human-Robot Interaction: A Review of Manipulations and Outcomes

2009 ◽  
Author(s):  
Matthew S. Prewett ◽  
Kristin N. Saboe ◽  
Ryan C. Johnson ◽  
Michael D. Coovert ◽  
Linda R. Elliott
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction

Biomimetic, sociable robots for human-robot interaction

2010 ◽  
Author(s):  
Eleanore Edson ◽  
Judith Lytle ◽  
Thomas McKenna
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction

The role and relationship of mindreading and social attunement in HRI – position statements of interdisciplinary researchers. Workshop HRI'20

2020 ◽  
Author(s):  
Agnieszka Wykowska ◽  
Jairo Pérez-Osorio ◽  
Stefan Kopp
Keyword(s):  
Mental States ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Artificial Agents ◽  
The Novel ◽  
Robot Interaction ◽  
Novel Coronavirus ◽  
Relationship Of ◽  
The Relationship

This booklet is a collection of the position statements accepted for the HRI’20 conference workshop “Social Cognition for HRI: Exploring the relationship between mindreading and social attunement in human-robot interaction” (Wykowska, Perez-Osorio & Kopp, 2020). Unfortunately, due to the rapid unfolding of the novel coronavirus at the beginning of the present year, the conference and consequently our workshop, were canceled. On the light of these events, we decided to put together the positions statements accepted for the workshop. The contributions collected in these pages highlight the role of attribution of mental states to artificial agents in human-robot interaction, and precisely the quality and presence of social attunement mechanisms that are known to make human interaction smooth, efficient, and robust. These papers also accentuate the importance of the multidisciplinary approach to advance the understanding of the factors and the consequences of social interactions with artificial agents.

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.

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