Recent advancements in multimodal human–robot interaction

Su, Hang; Qi, Wen; Yang, Chenguang; Sandoval, Juan; Laribi, Med Amine

doi:10.3389/fnbot.2023.1084000

Cited by 25 publications

(5 citation statements)

References 155 publications

(176 reference statements)

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“…In HRI, this capability allows robots to grasp the subtle nuances of emotional expression, leading to more contextually appropriate responses. As a result, the communication between humans and robots becomes richer and more meaningful, resembling the nuanced exchanges that occur in human-human interactions (Su et al, 2023).…”

Section: Facilitating Communicationmentioning

confidence: 99%

Emotion Recognition in Psychology of Human-robot Interaction

Zhao

2023

Psychomachina

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The field of Human-Robot Interaction (HRI) has garnered significant attention in recent years, with researchers and practitioners seeking to understand the psychological aspects underlying the interactions between humans and robots. One crucial area of focus within HRI is the psychology of emotion recognition, which plays a fundamental role in shaping the dynamics of human-robot interaction. This paper provides an overview of the background of psychology in the context of human-robot interaction, emphasizing the significance of understanding human emotions in this domain. The concept of emotion recognition, a key component of human psychology, is explored in detail, highlighting its relevance in the context of human-robot interaction. Emotion recognition allows robots to perceive and interpret human emotions, enabling them to respond appropriately and enhance the quality of interaction. The role of emotion recognition in HRI is examined from a psychological standpoint, shedding light on its implications for the design and development of effective human-robot interfaces. Furthermore, this paper delves into the application of machine learning techniques for emotion recognition in the context of human-robot interaction. Machine learning algorithms have shown promise in enabling robots to recognize and respond to human emotions, thereby contributing to more natural and intuitive interactions. The utilization of machine learning in emotion recognition reflects the intersection of psychology and technological advancements in the field of HRI. Finally, the challenges associated with emotion recognition in HRI are discussed, encompassing issues such as cross-cultural variations in emotional expression, individual differences, and the ethical implications of emotion detection. Addressing these challenges is pivotal in advancing the understanding and implementation of emotion recognition in human-robot interaction, underscoring the interdisciplinary nature of this endeavor. In conclusion, this paper underscores the critical role of emotion recognition in the psychology of human-robot interaction, emphasizing its potential to revolutionize the way humans and robots engage with each other. By integrating insights from psychology, machine learning, and technology, advancements in emotion recognition have the potential to pave the way for more empathetic and responsive human-robot interactions, offering new avenues for research and practical applications in this burgeoning field.

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Section: Facilitating Communicationmentioning

confidence: 99%

Emotion Recognition in Psychology of Human-robot Interaction

Zhao

2023

Psychomachina

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“…Adjustable seating positions and customizable control interfaces have been incorporated to enhance user comfort and satisfaction (Avutu et al, 2023). Feedback and communication mechanisms, including auditory, visual, and haptic feedback, enable effective communication between the user and the wheelchair, conveying vital information about the wheelchair's status and environment (Su et al, 2023). Safety and trust are paramount, and features such as collision detection, emergency stop mechanisms, and failsafe systems ensure user safety and build trust in the technology (Zacharaki et al, 2020).…”

Section: Previous Studies On Human-robot Interactionmentioning

confidence: 99%

A Review on Human-Robot Interaction and User Experience in Smart Robotic Wheelchairs

Sahoo,

Choudhury

2023

JTIE

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This review research paper provides an analysis of the current state of Human-Robot Interaction (HRI) and User Experience (UX) in the context of smart robotic wheelchairs. It explores the advancements in HRI techniques, including multimodal interfaces, gesture recognition, voice commands, and brain-computer interfaces, and evaluates their impact on user experience factors such as usability, learnability, efficiency, and satisfaction. The paper discusses the role of artificial intelligence and machine learning in enhancing HRI capabilities and personalization of wheelchair behavior. The review highlights gaps in current research and identifies future directions to improve the immersive experience of smart wheelchair users. Overall, this comprehensive review contributes to a deeper understanding of the factors influencing user acceptance, satisfaction, and system performance, guiding the development of more intuitive and user-centered smart robotic wheelchairs for individuals with mobility impairments.

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“…The exploration of human-robot interaction (HRI) [29], [32] and its advancement into real-world applications has been a topic of significant research over the past decades [30]. Current approaches for controlling and interacting with autonomous robots in the real 1 https://github.com/LinusNEP/TCC_IRoNL.git world have been dominated by complex teleoperation controllers [13], teach pendants [2], and rigid command protocols [16], where the robots execute predefined tasks based on specialized programming languages.…”

Section: Introductionmentioning

confidence: 99%

The Conversation is the Command: Interacting with Real-World Autonomous Robots Through Natural Language

Nwankwo,

Rueckert

2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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In recent years, autonomous agents have surged in real-world environments such as our homes, offices, and public spaces. However, natural human-robot interaction remains a key challenge. In this paper, we introduce an approach that synergistically exploits the capabilities of large language models (LLMs) and multimodal vision-language models (VLMs) to enable humans to interact naturally with autonomous robots through conversational dialogue. We leveraged the LLMs to decode the high-level natural language instructions from humans and abstract them into precise robot actionable commands or queries. Further, we utilised the VLMs to provide a visual and semantic understanding of the robot's task environment. Our results with 99.13% command recognition accuracy and 97.96% commands execution success show that our approach can enhance human-robot interaction in real-world applications. The video demonstrations of this paper can be found at https://osf.io/wzyf6 and the code is available at our repository 1 . CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Interaction paradigms; Natural language interfaces;

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Recent advancements in multimodal human–robot interaction

Cited by 25 publications

References 155 publications

Emotion Recognition in Psychology of Human-robot Interaction

Emotion Recognition in Psychology of Human-robot Interaction

A Review on Human-Robot Interaction and User Experience in Smart Robotic Wheelchairs

The Conversation is the Command: Interacting with Real-World Autonomous Robots Through Natural Language

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