Enhancing Supermarket Robot Interaction: A Multi-Level LLM Conversational Interface for Handling Diverse Customer Intents

This paper presents the design and evaluation of a novel multi-level LLM interface for supermarket robots to assist customers. The proposed interface allows customers to convey their needs through both generic and specific queries. While state-of-the-art systems like OpenAI's GPTs are highly adaptable and easy to build and deploy, they still face challenges such as increased response times and limitations in strategic control of the underlying model for tailored use-case and cost optimization. Driven by the goal of developing faster and more efficient conversational agents, this paper advocates for using multiple smaller, specialized LLMs fine-tuned to handle different user queries based on their specificity and user intent. We compare this approach to a specialized GPT model powered by GPT-4 Turbo, using the Artificial Social Agent Questionnaire (ASAQ) and qualitative participant feedback in a counterbalanced within-subjects experiment. Our findings show that our multi-LLM chatbot architecture outperformed the benchmarked GPT model across all 13 measured criteria, with statistically significant improvements in four key areas: performance, user satisfaction, user-agent partnership, and self-image enhancement. The paper also presents a method for supermarket robot navigation by mapping the final chatbot response to correct shelf numbers, enabling the robot to sequentially navigate towards the respective products, after which lower-level robot perception, control, and planning can be used for automated object retrieval. We hope this work encourages more efforts into using multiple, specialized smaller models instead of relying on a single powerful, but more expensive and slower model

Enhancing supermarket robot interaction: an equitable multi-level LLM conversational interface for handling diverse customer intents

This paper presents the design and evaluation of a comprehensive system to develop voice-based interfaces to support users in supermarkets. These interfaces enable shoppers to convey their needs through both generic and specific queries. Although customisable state-of-the-art systems like GPTs from OpenAI are easily accessible and adaptable, featuring low-code deployment with options for functional integration, they still face challenges such as increased response times and limitations in strategic control for tailored use cases and cost optimization. Motivated by the goal of crafting equitable and efficient conversational agents with a touch of personalisation, this study advances on two fronts: 1) a comparative analysis of four popular off-the-shelf speech recognition technologies to identify the most accurate model for different genders (male/female) and languages (English/Dutch) and 2) the development and evaluation of a novel multi-LLM supermarket chatbot framework, comparing its performance with a specialized GPT model powered by the GPT-4 Turbo, using the Artificial Social Agent Questionnaire (ASAQ) and qualitative participant feedback. Our findings reveal that OpenAI’s Whisper leads in speech recognition accuracy between genders and languages and that our proposed multi-LLM chatbot architecture, which outperformed the benchmarked GPT model in performance, user satisfaction, user-agent partnership, and self-image enhancement, achieved statistical significance in these four key areas out of the 13 evaluated aspects that all showed improvements. The paper concludes with a simple method for supermarket robot navigation by mapping the final chatbot response to the correct shelf numbers to which the robot can plan sequential visits. Later, this enables the effective use of low-level perception, motion planning, and control capabilities for product retrieval and collection. We hope that this work encourages more efforts to use multiple specialized smaller models instead of always relying on a single powerful model

The Collagen Suprafamily: From Biosynthesis to Advanced Biomaterial Development

Collagen is the oldest and most abundant extracellular matrix protein that has found many applications in food, cosmetic, pharmaceutical, and biomedical industries. First, an overview of the family of collagens and their respective structures, conformation, and biosynthesis is provided. The advances and shortfalls of various collagen preparations (e.g., mammalian/marine extracted collagen, cell-produced collagens, recombinant collagens, and collagen-like peptides) and crosslinking technologies (e.g., chemical, physical, and biological) are then critically discussed. Subsequently, an array of structural, thermal, mechanical, biochemical, and biological assays is examined, which are developed to analyze and characterize collagenous structures. Lastly, a comprehensive review is provided on how advances in engineering, chemistry, and biology have enabled the development of bioactive, 3D structures (e.g., tissue grafts, biomaterials, cell-assembled tissue equivalents) that closely imitate native supramolecular assemblies and have the capacity to deliver in a localized and sustained manner viable cell populations and/or bioactive/therapeutic molecules. Clearly, collagens have a long history in both evolution and biotechnology and continue to offer both challenges and exciting opportunities in regenerative medicine as nature's biomaterial of choice.This work forms part of the Teagasc Walsh Fellowship (grant award number: 2014045) and the ReValueProtein Research Project (grant award number: 11/F/043) supported by the Department of Agriculture, Food and the Marine (DAFM) under the National Development Plan 2007–2013 funded by the Irish Government. This work has also been supported from the: Health Research Board, Health Research Awards Programme (grant agreement number: HRA_POR/2011/84); Science Foundation Ireland, Career Development Award Programme (grant agreement number: 15/CDA/3629); Science Foundation Ireland and the European Regional Development Fund (grant agreement number: 13/RC/2073); College of Engineering and Informatics, National University of Ireland Galway; EU FP7/2007-2013, NMP award, Green Nano Mesh Project (grant agreement number: 263289); EU FP7/2007-2013, Health award, Neurograft Project (grant agreement number: 304936); EU H2020, ITN award, Tendon Therapy Train Project (grant agreement number: 676338); National University of Singapore Tissue Engineering Programme (NUSTEP). The authors would like to thank M Doczyk, E Collin, W Daly, M Abu-Rub, D Thomas, S Browne, C Tapeinos, A Satyam and D Cigognini for their help in producing the figures. A.S., L.M.D., Z.W., N.S., A.K., R.N.R., A.M.M., A.P., M.R., and D.I.Z. have no competing interests. Y.B. is an employee of Sofradim Production – A Medtronic Company. D.I.Z would like to dedicate the manuscript to A.G.Z. who left and A.D.Z. who camepeer-reviewe

The collagen suprafamily : from biosynthesis to advanced biomaterial development

Biomimetic microenvironments are key components to successful cell culture and tissue engineering in vitro. One of the most accurate biomimetic microenvironments is that made by the cells themselves. Cell-made microenvironments are most similar to the in vivo state as they are cell-specific and produced by the actual cells which reside in that specific microenvironment. However, cell-made microenvironments have been challenging to re-create in vitro due to the lack of extracellular matrix composition, volume and complexity which are required. By applying macromolecular crowding to current cell culture protocols, cell-made microenvironments, or cell-derived matrices, can be generated at significant rates in vitro. In this review, we will examine the causes and effects of macromolecular crowding and how it has been applied in several in vitro systems including tissue engineering