Concetti Chiave
Domain-specific large vision models have the potential to significantly enhance the performance and robustness of human-robot interaction systems compared to conventional computer vision models.
Sintesi
This paper introduces an initial design space that incorporates domain-specific large vision models (LVMs) for human-robot interaction (HRI) systems. The design space consists of three primary dimensions: HRI contexts, vision-based tasks, and specific domains.
The HRI contexts dimension covers three categories: human-initiated, robot-proactive, and neutral interactions. The vision-based tasks dimension includes nine tasks such as visual detection, recognition, segmentation, tracking, classification, and generation. The specific domains dimension outlines eight areas where LVMs can be particularly beneficial, including healthcare, automotive, manufacturing, entertainment, security, agriculture, education, and social interaction.
The authors conducted an empirical validation with 15 expert participants, evaluating the design space across six metrics: perceived likeability, trustworthiness, usefulness, intent to use, comprehensiveness, and system usability. The results showed that the HRI contexts dimension received the highest ratings, while the vision-based tasks dimension was evaluated as the least effective.
The paper highlights the advantages of domain-specific LVMs over normal LVMs and commonly-used vision models, such as reduced training costs, enhanced accuracy within specific domains, and unprecedented scale via extensive parameters for fine-tuning. The authors envision this design space as a foundational guideline for future HRI system design, emphasizing accurate domain alignment and model selection.
The paper also discusses the challenges and limitations of the initial design space, including the need to address ethical considerations, data bias, and model interpretability. Future research directions involve incorporating user feedback, exploring new vision tasks and domains, and further validating the design space in real-world HRI applications.
Statistiche
"The emergence of LVMs has opened the potential to address long-standing challenges in HRI through their advanced capabilities in visual perception and interpretation."
"Domain-specific LVMs have shown superior efficacy in comprehending the unique and nuanced visual content relevant to particular contexts, excelling in tasks such as visual classification, object detection, and segmentation which are pervasively utilized in recent HRI architectures."
"According to Landing AI, the LVM revolution is trailing LLM by two or three years, while domain-specific LVMs outperform others."
Citazioni
"The emergence of LVMs has opened the potential to address long-standing challenges in HRI through their advanced capabilities in visual perception and interpretation."
"Domain-specific LVMs have shown superior efficacy in comprehending the unique and nuanced visual content relevant to particular contexts, excelling in tasks such as visual classification, object detection, and segmentation which are pervasively utilized in recent HRI architectures."
"According to Landing AI, the LVM revolution is trailing LLM by two or three years, while domain-specific LVMs outperform others."