scholarly journals The Effectiveness of Dynamically Processed Incremental Descriptions in Human Robot Interaction

2022 ◽  
Vol 11 (1) ◽  
pp. 1-24
Author(s):  
Christopher D. Wallbridge ◽  
Alex Smith ◽  
Manuel Giuliani ◽  
Chris Melhuish ◽  
Tony Belpaeme ◽  
...  
Keyword(s):  
Dynamic System ◽  
Nuclear Waste ◽  
Efficient Method ◽  
Real World ◽  
Human Robot Interaction ◽  
User Preference ◽  
Generation System ◽  
Robot Interaction ◽  
Distance Measurements ◽  
Real World Situation

We explore the effectiveness of a dynamically processed incremental referring description system using under-specified ambiguous descriptions that are then built upon using linguistic repair statements, which we refer to as a dynamic system. We build a dynamically processed incremental referring description generation system that is able to provide contextual navigational statements to describe an object in a potential real-world situation of nuclear waste sorting and maintenance. In a study of 31 participants, we test the dynamic system in a case where a user is remote operating a robot to sort nuclear waste, with the robot assisting them in identifying the correct barrels to be removed. We compare these against a static non-ambiguous description given in the same scenario. As well as looking at efficiency with time and distance measurements, we also look at user preference. Results show that our dynamic system was a much more efficient method—taking only 62% of the time on average—for finding the correct barrel. Participants also favoured our dynamic system.

scholarly journals GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Robotic Manipulator ◽  
Similarity Score ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
The Other ◽  
Robot Interaction ◽  
Run Length ◽  
The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

Affective Goal and Task Selection for Social Robots

2009 ◽  
pp. 74-87 ◽  
Author(s):  
Matthias Scheutz ◽  
Paul Schermerhorn
Keyword(s):  
Decision Making ◽  
Real World ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Task Selection ◽  
Resource Limitations ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Selection Of

Effective decision-making under real-world conditions can be very difficult as purely rational methods of decision-making are often not feasible or applicable. Psychologists have long hypothesized that humans are able to cope with time and resource limitations by employing affective evaluations rather than rational ones. In this chapter, we present the distributed integrated affect cognition and reflection architecture DIARC for social robots intended for natural human-robot interaction and demonstrate the utility of its human-inspired affect mechanisms for the selection of tasks and goals. Specifically, we show that DIARC incorporates affect mechanisms throughout the architecture, which are based on “evaluation signals” generated in each architectural component to obtain quick and efficient estimates of the state of the component, and illustrate the operation and utility of these mechanisms with examples from human-robot interaction experiments.

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.

scholarly journals Short-Term Human–Robot Interaction Adaptability in Real-World Environments

2019 ◽  
Vol 12 (3) ◽  
pp. 639-657 ◽  
Author(s):  
Antonio Andriella ◽  
Carme Torras ◽  
Guillem Alenyà
Keyword(s):  
Real World ◽  
Human Robot Interaction ◽  
Short Term ◽  
Robot Interaction

scholarly journals AIBOStory – Autonomous Robots supporting Interactive, Collaborative Story-telling

2013 ◽  
Vol 4 (1) ◽  
Author(s):  
Fotios Papadopoulos ◽  
Kerstin Dautenhahn ◽  
Wan Ching Ho
Keyword(s):  
Autonomous Robots ◽  
Autonomous Robot ◽  
User Preferences ◽  
Human Robot Interaction ◽  
User Preference ◽  
Story Telling ◽  
Remote Communication ◽  
Robot Interaction ◽  
Communication Software

AbstractThis article describes the design and evaluation of AIBOStory - a novel, remote interactive story telling system that allows users to create and share common stories through an integrated, autonomous robot companion acting as a social mediator between two remotely located people. The behaviour of the robot was inspired by dog behaviour, including a simple computational memory model. AIBOStory has been designed to work alongside online video communication software and aims to enrich remote communication experiences over the internet. An initial pilot study evaluated the proposed system’s use and acceptance by the users. Five pairs of participants were exposed to the system, with the robot acting as a social mediator, and the results suggested an overall positive acceptance response. The main study involved long-term interactions of 20 participants using AIBOStory in order to study their preferences between two modes: using the game enhanced with an autonomous robot and a non-robot mode which did not use the robot. Instruments used in this study include multiple questionnaires from different communication sessions, demographic forms and logged data from the robots and the system. The data was analysed using quantitative and qualitative techniques to measure user preference and human-robot interaction. The statistical analysis suggests user preferences towards the robot mode.

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