Human robot interaction can boost robot's affordance learning: A proof of concept

Author(s):  
Amit Kumar Pandey ◽  
Rodolphe Gelin
Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 112
Author(s):  
Marit Hagens ◽  
Serge Thill

Perfect information about an environment allows a robot to plan its actions optimally, but often requires significant investments into sensors and possibly infrastructure. In applications relevant to human–robot interaction, the environment is by definition dynamic and events close to the robot may be more relevant than distal ones. This suggests a non-trivial relationship between sensory sophistication on one hand, and task performance on the other. In this paper, we investigate this relationship in a simulated crowd navigation task. We use three different environments with unique characteristics that a crowd navigating robot might encounter and explore how the robot’s sensor range correlates with performance in the navigation task. We find diminishing returns of increased range in our particular case, suggesting that task performance and sensory sophistication might follow non-trivial relationships and that increased sophistication on the sensor side does not necessarily equal a corresponding increase in performance. Although this result is a simple proof of concept, it illustrates the benefit of exploring the consequences of different hardware designs—rather than merely algorithmic choices—in simulation first. We also find surprisingly good performance in the navigation task, including a low number of collisions with simulated human agents, using a relatively simple A*/NavMesh-based navigation strategy, which suggests that navigation strategies for robots in crowds need not always be sophisticated.


2021 ◽  
Vol 12 (1) ◽  
pp. 402-422
Author(s):  
Kheng Lee Koay ◽  
Matt Webster ◽  
Clare Dixon ◽  
Paul Gainer ◽  
Dag Syrdal ◽  
...  

Abstract When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human–robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human–robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants’ views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants’ technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation.


2019 ◽  
Vol 374 (1771) ◽  
pp. 20180433 ◽  
Author(s):  
Emily C. Collins

This opinion paper discusses how human–robot interaction (HRI) methodologies can be robustly developed by drawing on insights from fields outside of HRI that explore human–other interactions. The paper presents a framework that draws parallels between HRIs, and human–human, human–animal and human–object interaction literature, by considering the morphology and use of a robot to aid the development of robust HRI methodologies. The paper then briefly presents some novel empirical work as proof of concept to exemplify how the framework can help researchers define the mechanism of effect taking place within specific HRIs. The empirical work draws on known mechanisms of effect in animal-assisted therapy, and behavioural observations of touch patterns and their relation to individual differences in caring and attachment styles, and details how this trans-disciplinary approach to HRI methodology development was used to explore how an interaction with an animal-like robot was impacting a user. In doing so, this opinion piece outlines how useful objective, psychological measures of social cognition can be for deepening our understanding of HRI, and developing richer HRI methodologies, which take us away from questions that simply ask ‘Is this a good robot?’, and closer towards questions that ask ‘What mechanism of effect is occurring here, through which effective HRI is being performed?’ This paper further proposes that in using trans-disciplinary methodologies, experimental HRI can also be used to study human social cognition in and of itself. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.


2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
José Carlos Castillo ◽  
Fernando Alonso-Martín ◽  
David Cáceres-Domínguez ◽  
María Malfaz ◽  
Miguel A. Salichs

Human communication relies on several aspects beyond the speech. One of them is gestures as they express intentions, interests, feelings, or ideas and complement the speech. Social robots need to interpret these messages to allow a more natural Human-Robot Interaction. In this sense, our aim is to study the effect of position and speed features in dynamic gesture recognition. We use 3D information to extract the user’s skeleton and calculate the normalized position for all of its joints, and using the temporal variation of such positions, we calculate their speeds. Our three datasets are composed of 1355 samples from 30 users. We consider 14 common gestures in HRI involving upper body movements. A set of classification techniques is evaluated testing these three datasets to find what features perform better. Results indicate that the union of both speed and position achieves the best results among the three possibilities, 0.999 of F-score. The combination that performs better to detect dynamic gestures in real time is finally integrated in our social robot with a simple HRI application to run a proof of concept test to check how the proposal behaves in a realistic scenario.


2021 ◽  
Vol 12 ◽  
Author(s):  
Gregoire Pointeau ◽  
Solène Mirliaz ◽  
Anne-Laure Mealier ◽  
Peter Ford Dominey

How do people learn to talk about the causal and temporal relations between events, and the motivation behind why people do what they do? The narrative practice hypothesis of Hutto and Gallagher holds that children are exposed to narratives that provide training for understanding and expressing reasons for why people behave as they do. In this context, we have recently developed a model of narrative processing where a structured model of the developing situation (the situation model) is built up from experienced events, and enriched by sentences in a narrative that describe event meanings. The main interest is to develop a proof of concept for how narrative can be used to structure, organize and describe experience. Narrative sentences describe events, and they also define temporal and causal relations between events. These relations are specified by a class of narrative function words, including “because, before, after, first, finally.” The current research develops a proof of concept that by observing how people describe social events, a developmental robotic system can begin to acquire early knowledge of how to explain the reasons for events. We collect data from naïve subjects who use narrative function words to describe simple scenes of human-robot interaction, and then employ algorithms for extracting the statistical structure of how narrative function words link events in the situation model. By using these statistical regularities, the robot can thus learn from human experience about how to properly employ in question-answering dialogues with the human, and in generating canonical narratives for new experiences. The behavior of the system is demonstrated over several behavioral interactions, and associated narrative interaction sessions, while a more formal extended evaluation and user study will be the subject of future research. Clearly this is far removed from the power of the full blown narrative practice capability, but it provides a first step in the development of an experimental infrastructure for the study of socially situated narrative practice in human-robot interaction.


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

2010 ◽  
Author(s):  
Eleanore Edson ◽  
Judith Lytle ◽  
Thomas McKenna

2020 ◽  
Author(s):  
Agnieszka Wykowska ◽  
Jairo Pérez-Osorio ◽  
Stefan Kopp

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.


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