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    Human-Robot Interaction (HRI)

    Human-Robot Interaction (HRI) is a multidisciplinary field that aims to create seamless and effective communication between humans and robots.

    HRI research focuses on developing natural and intuitive interactions, including both verbal and nonverbal communication. One prevalent nonverbal communication approach is the use of hand and arm gestures, which are ubiquitous in daily life. Researchers in HRI have been working on various aspects of gesture-based interaction, such as generating human gestures, enabling robots to recognize these gestures, and designing appropriate robot responses.

    Recent advancements in HRI have been driven by the integration of artificial intelligence (AI) techniques. The AI-HRI community has been exploring various topics, such as trust in HRI, explainable AI for HRI, and service robots. The community has also been investigating the ethical aspects of HRI, as ethics is an inherent part of human-robot interaction.

    One of the challenges in HRI research is the design of human-subjects studies, which are essential for collecting data to train machine learning models. Researchers have proposed a clearly defined process for data collection, consisting of three steps: defining the data collection goal, designing the task environment and procedure, and encouraging well-covered and abundant participant responses.

    Practical applications of HRI research include:

    1. Service robots: Robots that assist humans in various tasks, such as cleaning, cooking, or healthcare.

    2. Industrial automation: Robots that work alongside humans in factories, improving efficiency and safety.

    3. Assistive technologies: Robots that help people with disabilities, such as mobility aids or communication devices.

    A company case study in HRI is HAVEN, a virtual reality (VR) simulation that enables users to interact with a virtual robot. HAVEN was developed in response to the COVID-19 pandemic, which made in-person HRI studies difficult due to social distancing requirements. The system allows researchers to conduct HRI augmented reality studies using a virtual robot without being in a real environment.

    In conclusion, HRI research is a rapidly evolving field that combines AI techniques with human-centered design principles to create natural and effective communication between humans and robots. As the field continues to advance, it is expected to have a significant impact on various industries and applications, ultimately improving the quality of human life.

    What is Human-Robot Interaction (HRI)?

    Human-Robot Interaction (HRI) is a multidisciplinary field that focuses on creating seamless and effective communication between humans and robots. It involves the development of natural and intuitive interactions, including both verbal and nonverbal communication, to enable humans and robots to work together efficiently and safely.

    How does artificial intelligence (AI) contribute to HRI?

    AI plays a significant role in advancing HRI by providing techniques for robots to understand and interpret human behavior, make decisions, and learn from experience. AI techniques, such as machine learning and natural language processing, enable robots to recognize human gestures, understand spoken commands, and respond appropriately to various situations.

    What are the ethical considerations in HRI?

    Ethical considerations in HRI include ensuring the safety and well-being of humans, respecting privacy, and avoiding biases in robot behavior. Researchers in HRI are also concerned with developing robots that can understand and adhere to social norms, as well as designing systems that promote trust and transparency between humans and robots.

    How are human-subjects studies conducted in HRI research?

    Human-subjects studies are essential in HRI research for collecting data to train machine learning models and evaluate robot performance. These studies typically involve a clearly defined process, including defining the data collection goal, designing the task environment and procedure, and encouraging well-covered and abundant participant responses. Researchers must also consider ethical guidelines and obtain informed consent from participants.

    What is the role of gesture-based interaction in HRI?

    Gesture-based interaction is a prevalent nonverbal communication approach in HRI that involves the use of hand and arm gestures. Researchers work on various aspects of gesture-based interaction, such as generating human gestures, enabling robots to recognize these gestures, and designing appropriate robot responses. This approach helps create more natural and intuitive communication between humans and robots.

    Can you provide an example of a company case study in HRI?

    HAVEN is a virtual reality (VR) simulation developed in response to the COVID-19 pandemic, which made in-person HRI studies difficult due to social distancing requirements. HAVEN enables users to interact with a virtual robot, allowing researchers to conduct HRI augmented reality studies without being in a real environment. This system demonstrates how technology can adapt to challenges and continue advancing HRI research.

    What are the future directions of HRI research?

    Future directions in HRI research include further integration of AI techniques, improving robot autonomy, and addressing ethical concerns. Researchers will also focus on developing robots that can better understand and adapt to human emotions, intentions, and social cues. As HRI continues to advance, it is expected to have a significant impact on various industries and applications, ultimately improving the quality of human life.

    Human-Robot Interaction (HRI) Further Reading

    1.AI-HRI 2021 Proceedings http://arxiv.org/abs/2109.10836v2 Reuth Mirsky, Megan Zimmerman, Muneed Ahmad, Shelly Bagchi, Felix Gervits, Zhao Han, Justin Hart, Daniel Hernández García, Matteo Leonetti, Ross Mead, Emmanuel Senft, Jivko Sinapov, Jason Wilson
    2.Championing Research Through Design in HRI http://arxiv.org/abs/1908.07572v1 Michal Luria, John Zimmerman, Jodi Forlizzi
    3.Hand and Arm Gesture-based Human-Robot Interaction: A Review http://arxiv.org/abs/2209.08229v1 Xihao Wang, Hao Shen, Hui Yu, Jielong Guo, Xian Wei
    4.AI-HRI Brings New Dimensions to Human-Aware Design for Human-Aware AI http://arxiv.org/abs/2210.11832v1 Richard G. Freedman
    5.HAVEN: A Unity-based Virtual Robot Environment to Showcase HRI-based Augmented Reality http://arxiv.org/abs/2011.03464v1 Andre Cleaver, Darren Tang, Victoria Chen, Jivko Sinapov
    6.User, Robot, Deployer: A New Model for Measuring Trust in HRI http://arxiv.org/abs/2109.00861v1 David Cameron, Emily C. Collins
    7.Proceedings of the AI-HRI Symposium at AAAI-FSS 2020 http://arxiv.org/abs/2010.13830v4 Shelly Bagchi, Jason R. Wilson, Muneeb I. Ahmad, Christian Dondrup, Zhao Han, Justin W. Hart, Matteo Leonetti, Katrin Lohan, Ross Mead, Emmanuel Senft, Jivko Sinapov, Megan L. Zimmerman
    8.Proceedings of the AI-HRI Symposium at AAAI-FSS 2022 http://arxiv.org/abs/2209.14292v3 Zhao Han, Emmanuel Senft, Muneeb I. Ahmad, Shelly Bagchi, Amir Yazdani, Jason R. Wilson, Boyoung Kim, Ruchen Wen, Justin W. Hart, Daniel Hernández García, Matteo Leonetti, Ross Mead, Reuth Mirsky, Ahalya Prabhakar, Megan L. Zimmerman
    9.Proceedings of the AI-HRI Symposium at AAAI-FSS 2018 http://arxiv.org/abs/1809.06606v1 Kalesha Bullard, Nick DePalma, Richard G. Freedman, Bradley Hayes, Luca Iocchi, Katrin Lohan, Ross Mead, Emmanuel Senft, Tom Williams
    10.Towards Formalizing HRI Data Collection Processes http://arxiv.org/abs/2203.08396v1 Zhao Han, Tom Williams

    Explore More Machine Learning Terms & Concepts

    Human-Object Interaction

    Human-Object Interaction: Understanding and optimizing the complex relationships between humans and objects in various domains. Human-Object Interaction (HOI) is a multidisciplinary field that focuses on understanding and optimizing the complex relationships between humans and objects in various domains, such as e-commerce, online education, social networks, and interactive visualizations. By studying these interactions, researchers can develop more effective and user-friendly systems, products, and services. One of the key challenges in HOI is to synthesize information from different sources and connect themes across various domains. This requires a deep understanding of the nuances and complexities of human behavior, as well as the ability to model and predict interactions between humans and objects. Machine learning techniques, such as network embedding and graph attention networks, have been employed to mine information from temporal interaction networks and identify patterns in human-object interactions. Recent research in the field has explored various aspects of HOI, such as multi-relation aware temporal interaction network embedding (MRATE), which mines historical interaction relations, common interaction relations, and interaction sequence similarity relations to obtain neighbor-based embeddings of interacting nodes. Another study investigated the optimization of higher-order network topology for synchronization of coupled phase oscillators, revealing distinct properties of networks with 2-hyperlink interactions compared to 1-hyperlink (pairwise) interactions. Practical applications of HOI research can be found in numerous areas. For example, in e-commerce, understanding human-object interactions can help improve product recommendations and user experience. In online education, insights from HOI can be used to develop more engaging and effective learning materials. Additionally, in the field of interactive visualizations, incorporating data provenance can lead to the development of novel interactions and more intuitive user interfaces. A company case study that demonstrates the value of HOI research is the development of interactive furniture. By reimagining the ergonomics of interactive furniture and incorporating novel user experience design methods, companies can create products that better cater to the needs and preferences of users. In conclusion, Human-Object Interaction is a vital area of research that seeks to understand and optimize the complex relationships between humans and objects across various domains. By leveraging machine learning techniques and synthesizing information from different sources, researchers can gain valuable insights into the nuances and complexities of human-object interactions. These insights can then be applied to develop more effective and user-friendly systems, products, and services, ultimately benefiting both individuals and society as a whole.

    Hurdle Models

    Hurdle Models: A versatile approach for analyzing sparse and zero-inflated data. Hurdle models are a class of statistical models designed to handle data with an excess of zeros or other specific values, commonly found in fields such as economics, biology, and social sciences. These models are particularly useful for analyzing sparse data, where the presence of many zeros or other specific values can pose challenges for traditional statistical methods. The core idea behind hurdle models is to separate the data analysis process into two stages. In the first stage, the model focuses on the presence or absence of the specific value (e.g., zero) in the data. In the second stage, the model analyzes the non-zero or non-specific values, often using a different distribution or modeling approach. This two-stage process allows hurdle models to account for the unique characteristics of sparse data, providing more accurate and reliable results. Recent research has expanded the capabilities of hurdle models, integrating them with other statistical methods and machine learning techniques. For example, the low-rank hurdle model combines the hurdle approach with low-rank modeling to handle data with excess zeros or missing values. Another example is the ES Attack, a model stealing attack against deep neural networks that leverages hurdle models to overcome data hurdles and achieve functionally equivalent copies of victim models. Practical applications of hurdle models can be found in various domains. In manufacturing, they can be used for missing value imputation, improving the quality of data analysis. In the field of citation analysis, hurdle models can help researchers understand the factors that influence the chances of an article being highly cited. In the mining industry, hurdle models can be used to identify risk factors for workplace injuries, enabling the implementation of preventive measures. One company case study that demonstrates the value of hurdle models is the analysis of Italian tourism behavior during the Great Recession. Researchers used a multiple inflated negative binomial hurdle regression model to investigate the impact of the economic recession on the total number of overnight stays. The results provided valuable insights for policymakers seeking to support the tourism economy. In conclusion, hurdle models offer a versatile and powerful approach for analyzing sparse and zero-inflated data, addressing the challenges posed by traditional statistical methods. By integrating hurdle models with other techniques and applying them to various domains, researchers and practitioners can gain valuable insights and make more informed decisions.

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