With the rise in the number of robots in our daily lives, human-robot encounters will become more frequent. To improve human-robot interaction (HRI), people will require explanations of robots' actions, especially if they do something unexpected. Our focus is on robot navigation, where we explain why robots make specific navigational choices. Building on methods from the area of Explainable Artificial Intelligence (XAI), we employ a semantic map and techniques from the area of Qualitative Spatial Reasoning (QSR) to enrich visual explanations with knowledge-level spatial information. We outline how a robot can generate visual and textual explanations simultaneously and test our approach in simulation.
{"title":"Visuo-Textual Explanations of a Robot's Navigational Choices","authors":"Amar Halilovic, F. Lindner","doi":"10.1145/3568294.3580141","DOIUrl":"https://doi.org/10.1145/3568294.3580141","url":null,"abstract":"With the rise in the number of robots in our daily lives, human-robot encounters will become more frequent. To improve human-robot interaction (HRI), people will require explanations of robots' actions, especially if they do something unexpected. Our focus is on robot navigation, where we explain why robots make specific navigational choices. Building on methods from the area of Explainable Artificial Intelligence (XAI), we employ a semantic map and techniques from the area of Qualitative Spatial Reasoning (QSR) to enrich visual explanations with knowledge-level spatial information. We outline how a robot can generate visual and textual explanations simultaneously and test our approach in simulation.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76664176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in large scale language models have significantly changed the landscape of automatic dialogue systems and chatbots. We believe that these models also have a great potential for changing the way we interact with robots. Here, we present the first integration of the OpenAI GPT-3 language model for the Aldebaran Pepper and Nao robots. The present work transforms the text-based API of GPT-3 into an open verbal dialogue with the robots. The system will be presented live during the HRI2023 conference and the source code of this integration is shared with the hope that it will serve the community in designing and evaluating new dialogue systems for robots.
{"title":"Language Models for Human-Robot Interaction","authors":"E. Billing, Julia Rosén, M. Lamb","doi":"10.1145/3568294.3580040","DOIUrl":"https://doi.org/10.1145/3568294.3580040","url":null,"abstract":"Recent advances in large scale language models have significantly changed the landscape of automatic dialogue systems and chatbots. We believe that these models also have a great potential for changing the way we interact with robots. Here, we present the first integration of the OpenAI GPT-3 language model for the Aldebaran Pepper and Nao robots. The present work transforms the text-based API of GPT-3 into an open verbal dialogue with the robots. The system will be presented live during the HRI2023 conference and the source code of this integration is shared with the hope that it will serve the community in designing and evaluating new dialogue systems for robots.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78011440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The endogenous interruptions of smartphones have impacted people's everyday life in many aspects, especially in the study and work scene under a lamp. To mitigate this, we make a robot that could persuade you intrinsically by augmenting the lamp on your desk with specific posture and light. This paper will present our design considerations and the first prototype to show the possibility of alleviating people's endogenous interruptions through robots.
{"title":"A Persuasive Robot that Alleviates Endogenous Smartphone-related Interruption","authors":"Hanyang Hu, Mengyu Chen, Ruhan Wang, Yijie Guo","doi":"10.1145/3568294.3580097","DOIUrl":"https://doi.org/10.1145/3568294.3580097","url":null,"abstract":"The endogenous interruptions of smartphones have impacted people's everyday life in many aspects, especially in the study and work scene under a lamp. To mitigate this, we make a robot that could persuade you intrinsically by augmenting the lamp on your desk with specific posture and light. This paper will present our design considerations and the first prototype to show the possibility of alleviating people's endogenous interruptions through robots.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75616299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As robots become increasingly prevalent in our communities, aligning the values motivating their behavior with human values is critical. However, it is often difficult or impossible for humans, both expert and non-expert, to enumerate values comprehensively, accurately, and in forms that are readily usable for robot planning. Misspecification can lead to undesired, inefficient, or even dangerous behavior. In the value alignment problem, humans and robots work together to optimize human objectives, which are often represented as reward functions and which the robot can infer by observing human actions. In existing alignment approaches, no explicit feedback about this inference process is provided to the human. In this paper, we introduce an exploratory framework to address this problem, which we call Transparent Value Alignment (TVA). TVA suggests that techniques from explainable AI (XAI) be explicitly applied to provide humans with information about the robot's beliefs throughout learning, enabling efficient and effective human feedback.
{"title":"Transparent Value Alignment","authors":"Lindsay M. Sanneman, J. Shah","doi":"10.1145/3568294.3580147","DOIUrl":"https://doi.org/10.1145/3568294.3580147","url":null,"abstract":"As robots become increasingly prevalent in our communities, aligning the values motivating their behavior with human values is critical. However, it is often difficult or impossible for humans, both expert and non-expert, to enumerate values comprehensively, accurately, and in forms that are readily usable for robot planning. Misspecification can lead to undesired, inefficient, or even dangerous behavior. In the value alignment problem, humans and robots work together to optimize human objectives, which are often represented as reward functions and which the robot can infer by observing human actions. In existing alignment approaches, no explicit feedback about this inference process is provided to the human. In this paper, we introduce an exploratory framework to address this problem, which we call Transparent Value Alignment (TVA). TVA suggests that techniques from explainable AI (XAI) be explicitly applied to provide humans with information about the robot's beliefs throughout learning, enabling efficient and effective human feedback.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73759657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christine P. Lee, Bengisu Cagiltay, Dakota Sullivan, Bilge Mutlu
While social robots are increasingly introduced into domestic settings, few have explored the utility of the robots' packaging. Here we highlight the potential of product packaging in human-robot interaction to facilitate, expand, and enrich user experience with the robot. We present a social robot's box as interactive product packaging, designed to be reused as a "home'' for the robot. Through co-design sessions with children, an narrative-driven and socially engaging box was developed to support initial interactions between the child and the robot. Our findings emphasize the importance of packaging design to produce positive outcomes towards successful human-robot interaction.
{"title":"Demonstrating the Potential of Interactive Product Packaging for Enriching Human-Robot Interaction","authors":"Christine P. Lee, Bengisu Cagiltay, Dakota Sullivan, Bilge Mutlu","doi":"10.1145/3568294.3580038","DOIUrl":"https://doi.org/10.1145/3568294.3580038","url":null,"abstract":"While social robots are increasingly introduced into domestic settings, few have explored the utility of the robots' packaging. Here we highlight the potential of product packaging in human-robot interaction to facilitate, expand, and enrich user experience with the robot. We present a social robot's box as interactive product packaging, designed to be reused as a \"home'' for the robot. Through co-design sessions with children, an narrative-driven and socially engaging box was developed to support initial interactions between the child and the robot. Our findings emphasize the importance of packaging design to produce positive outcomes towards successful human-robot interaction.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73916274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hoi Ki Tang, Matthijs H. J. Smakman, M. De Haas, Rianne van den Berghe
Vocabulary is a crucial part of second language (L2) learning. Children learn new vocabulary by forming mental lexicon relations with their existing knowledge. This is called lexical inferencing: using the available clues and knowledge to guess the meaning of the unknown word. This study explored the potential of second language vocabulary acquisition through lexical inferencing in child-robot interaction. A storytelling robot read a book to Dutch kindergartners (N = 36, aged 4-6 years) in Dutch in which a few key words were translated into French (L2), and with a robot providing additional word explanation cues or not. The results showed that the children learned the key words successfully as a result of the reading session with the storytelling robot, but that there was no significant effect of additional word explanation cues by the robot. Overall, it seems promising that lexical inferencing can act as a new and different way to teach kindergartners a second language.
{"title":"L2 Vocabulary Learning Through Lexical Inferencing Stories With a Social Robot","authors":"Hoi Ki Tang, Matthijs H. J. Smakman, M. De Haas, Rianne van den Berghe","doi":"10.1145/3568294.3580140","DOIUrl":"https://doi.org/10.1145/3568294.3580140","url":null,"abstract":"Vocabulary is a crucial part of second language (L2) learning. Children learn new vocabulary by forming mental lexicon relations with their existing knowledge. This is called lexical inferencing: using the available clues and knowledge to guess the meaning of the unknown word. This study explored the potential of second language vocabulary acquisition through lexical inferencing in child-robot interaction. A storytelling robot read a book to Dutch kindergartners (N = 36, aged 4-6 years) in Dutch in which a few key words were translated into French (L2), and with a robot providing additional word explanation cues or not. The results showed that the children learned the key words successfully as a result of the reading session with the storytelling robot, but that there was no significant effect of additional word explanation cues by the robot. Overall, it seems promising that lexical inferencing can act as a new and different way to teach kindergartners a second language.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84558779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christine T. Chang, Mitchell Hebert, Bradley Hayes
Our work aims to apply iterative communication techniques to improve functionality of human-robot teams working in space and other high-risk environments. Forms of iterative communication include progressive incorporation of human preference and otherwise latent task specifications. Our prior work found that humans would choose not to comply with robot-provided instructions and then proceed to self-justify their choices despite the risks of physical harm and blatant disregard for rules. Results clearly showed that humans working near robots are willing to sacrifice safety for efficiency. Current work aims to improve communication by iteratively incorporating human preference into optimized path planning for human-robot teams operating over large areas. Future work will explore the extent to which negotiation can be used as a mechanism for improving task planning and joint task execution for humans and robots.
{"title":"Collaborative Planning and Negotiation in Human-Robot Teams","authors":"Christine T. Chang, Mitchell Hebert, Bradley Hayes","doi":"10.1145/3568294.3579978","DOIUrl":"https://doi.org/10.1145/3568294.3579978","url":null,"abstract":"Our work aims to apply iterative communication techniques to improve functionality of human-robot teams working in space and other high-risk environments. Forms of iterative communication include progressive incorporation of human preference and otherwise latent task specifications. Our prior work found that humans would choose not to comply with robot-provided instructions and then proceed to self-justify their choices despite the risks of physical harm and blatant disregard for rules. Results clearly showed that humans working near robots are willing to sacrifice safety for efficiency. Current work aims to improve communication by iteratively incorporating human preference into optimized path planning for human-robot teams operating over large areas. Future work will explore the extent to which negotiation can be used as a mechanism for improving task planning and joint task execution for humans and robots.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84917363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yermakhan Kassym, Saparkhan Kassymbekov, Kamila Zhumakhanova, A. Sandygulova
Drones are continually entering our daily lives by being used in a number of different applications. This creates a natural demand for better interaction ways between humans and drones. One of the possible applications that would benefit from improved interaction is the inspection of smoking in prohibited areas. We propose our own gesture of drone flight that we believe would deliver the message "not to smoke" better than the ready-made built-in gesture. To this end, we conducted a within-subject experiment involving 19 participants, where we evaluated the gestures on a drone operated through the Wizard-of-Oz interaction design. The results demonstrate that the proposed gesture was better at conveying the message compared to the built-in gesture.
{"title":"Human-Drone Interaction: Interacting with People Smoking in Prohibited Areas","authors":"Yermakhan Kassym, Saparkhan Kassymbekov, Kamila Zhumakhanova, A. Sandygulova","doi":"10.1145/3568294.3580173","DOIUrl":"https://doi.org/10.1145/3568294.3580173","url":null,"abstract":"Drones are continually entering our daily lives by being used in a number of different applications. This creates a natural demand for better interaction ways between humans and drones. One of the possible applications that would benefit from improved interaction is the inspection of smoking in prohibited areas. We propose our own gesture of drone flight that we believe would deliver the message \"not to smoke\" better than the ready-made built-in gesture. To this end, we conducted a within-subject experiment involving 19 participants, where we evaluated the gestures on a drone operated through the Wizard-of-Oz interaction design. The results demonstrate that the proposed gesture was better at conveying the message compared to the built-in gesture.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79818653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The prolificacy of human-robot interaction not only depends on a robot's ability to understand the intent and content of the human utterance but also gets impacted by the automatic speech recognition (ASR) system. Modern ASR can provide highly accurate (grammatically and syntactically) translation. Yet, the general purpose ASR often misses out on the semantics of the translation by incorrect word prediction due to open-vocabulary modeling. ASR inaccuracy can have significant repercussions as this can lead to a completely different action by the robot in the real world. Can any prior knowledge be helpful in such a scenario? In this work, we explore how prior knowledge can be utilized in ASR decoding. Using our experiments, we demonstrate how our system can significantly improve ASR translation for robotic task instruction.
{"title":"Utilizing Prior Knowledge to Improve Automatic Speech Recognition in Human-Robot Interactive Scenarios","authors":"Pradip Pramanick, Chayan Sarkar","doi":"10.1145/3568294.3580129","DOIUrl":"https://doi.org/10.1145/3568294.3580129","url":null,"abstract":"The prolificacy of human-robot interaction not only depends on a robot's ability to understand the intent and content of the human utterance but also gets impacted by the automatic speech recognition (ASR) system. Modern ASR can provide highly accurate (grammatically and syntactically) translation. Yet, the general purpose ASR often misses out on the semantics of the translation by incorrect word prediction due to open-vocabulary modeling. ASR inaccuracy can have significant repercussions as this can lead to a completely different action by the robot in the real world. Can any prior knowledge be helpful in such a scenario? In this work, we explore how prior knowledge can be utilized in ASR decoding. Using our experiments, we demonstrate how our system can significantly improve ASR translation for robotic task instruction.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84891050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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