At Your Service

Berardinis, Jacopo de; Pizzuto, Gabriella; Lanza, Francesco; Chella, Antonio; Meira, Jorge Augusto; Cangelosi, Angelo

doi:10.1145/3406499.3418765

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…An alternative approach is using partial Wizard-of-Oz (where the wizard responds to the users, but speech recognition is used for input) with a robot for data collection rather than a fully autonomous interaction where several repetitions and abrupt end of conversations could be encountered as observed in the barista robot study (Irfan et al, 2020a), which can make the data unusable for evaluations. Following these steps could help bring personalised robots, such as barista (Irfan et al, 2020a;de Berardinis et al, 2020) and bartender (Rossi and Rossi, 2021) robots within task-oriented domains, ready for real-world interaction through data-driven architectures.…”

Section: Discussionmentioning

confidence: 99%

Coffee With a Hint of Data: Towards Using Data-Driven Approaches in Personalised Long-Term Interactions

Irfan

Hellou²,

Belpaeme

2021

Front. Robot. AI

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While earlier research in human-robot interaction pre-dominantly uses rule-based architectures for natural language interaction, these approaches are not flexible enough for long-term interactions in the real world due to the large variation in user utterances. In contrast, data-driven approaches map the user input to the agent output directly, hence, provide more flexibility with these variations without requiring any set of rules. However, data-driven approaches are generally applied to single dialogue exchanges with a user and do not build up a memory over long-term conversation with different users, whereas long-term interactions require remembering users and their preferences incrementally and continuously and recalling previous interactions with users to adapt and personalise the interactions, known as the lifelong learning problem. In addition, it is desirable to learn user preferences from a few samples of interactions (i.e., few-shot learning). These are known to be challenging problems in machine learning, while they are trivial for rule-based approaches, creating a trade-off between flexibility and robustness. Correspondingly, in this work, we present the text-based Barista Datasets generated to evaluate the potential of data-driven approaches in generic and personalised long-term human-robot interactions with simulated real-world problems, such as recognition errors, incorrect recalls and changes to the user preferences. Based on these datasets, we explore the performance and the underlying inaccuracies of the state-of-the-art data-driven dialogue models that are strong baselines in other domains of personalisation in single interactions, namely Supervised Embeddings, Sequence-to-Sequence, End-to-End Memory Network, Key-Value Memory Network, and Generative Profile Memory Network. The experiments show that while data-driven approaches are suitable for generic task-oriented dialogue and real-time interactions, no model performs sufficiently well to be deployed in personalised long-term interactions in the real world, because of their inability to learn and use new identities, and their poor performance in recalling user-related data.

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Section: Discussionmentioning

confidence: 99%

Coffee With a Hint of Data: Towards Using Data-Driven Approaches in Personalised Long-Term Interactions

Irfan

Hellou²,

Belpaeme

2021

Front. Robot. AI

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“…In the service industry context, Berardinis et al [19] applied different supervised learning techniques for coffee recommendations based on users' preferences. Recently, Irfan et al [20] carried out a real-world study of fully autonomous interaction, evaluating the potential of data-driven approaches in generic and personalised long-term HRI.…”

Section: Related Workmentioning

confidence: 99%

Demonstration of a Robo- Barista for In the Wild Interactions

Lim¹,

Lopes

Robb³

et al. 2022

2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

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Studying Human-Robot Interaction over time can provide insights into what really happens when a robot becomes part of people's everyday lives. "In the Wild" studies inform the design of social robots, such as for the service industry, to enable them to remain engaging and useful beyond the novelty effect and initial adoption. This paper presents an "In the Wild" experiment where we explored the evolution of interaction between users and a Robo-Barista. We show that perceived trust and prior attitudes are both important factors associated with the usefulness, adaptability and likeability of the Robo-Barista. A combination of interaction features and user attributes are used to predict user satisfaction. Qualitative insights illuminated users' Robo-Barista experience and contribute to a number of lessons learned for future long-term studies.

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“…Establishing efficient collaboration between AI and unseen partners (either human players or AI agents), a concept known as zero-shot coordination (ZSC), continues to pose a significant challenge (Legg & Hutter, 2007;Hu et al, 2020;De Peuter & Kaski, 2022). The significance of zeroshot human-AI coordination becomes evident in various real-world applications, including manufacturing (Li et al, 2023a), autonomous vehicles (Aoki et al, 2021), and assistant robots (de Berardinis et al, 2020). The issue of "cooperative incompatibility" becomes particularly prominent when an AI agent is unsuccessful in synchronizing with certain previously unknown partners.…”

Section: Introductionmentioning

confidence: 99%

Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination

Li,

Zhang,

Sun

et al. 2024

jair

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Securing coordination between AI agent and teammates (human players or AI agents) in contexts involving unfamiliar humans continues to pose a significant challenge in Zero-Shot Coordination. The issue of cooperative incompatibility becomes particularly prominent when an AI agent is unsuccessful in synchronizing with certain previously unknown partners. Traditional algorithms have aimed to collaborate with partners by optimizing fixed objectives within a population, fostering diversity in strategies and behaviors. However, these techniques may lead to learning loss and an inability to cooperate with specific strategies within the population, a phenomenon named cooperative incompatibility in learning. In order to solve cooperative incompatibility in learning and effectively address the problem in the context of ZSC, we introduce the Cooperative Open-ended LEarning (COLE) framework, which formulates open-ended objectives in cooperative games with two players using perspectives of graph theory to evaluate and pinpoint the cooperative capacity of each strategy. We present two practical algorithms, specifically COLESV and COLER, which incorporate insights from game theory and graph theory. We also show that COLE could effectively overcome the cooperative incompatibility from theoretical and empirical analysis. Subsequently, we created an online Overcooked human-AI experiment platform, the COLE platform, which enables easy customization of questionnaires, model weights, and other aspects. Utilizing the COLE platform, we enlist 130 participants for human experiments. Our findings reveal a preference for our approach over state-of-the-art methods using a variety of subjective metrics. Moreover, objective experimental outcomes in the Overcooked game environment indicate that our method surpasses existing ones when coordinating with previously unencountered AI agents and the human proxy model. Our code and demo are publicly available at https://sites.google.com/view/cole-2023.

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At Your Service

Cited by 6 publications

References 6 publications

Coffee With a Hint of Data: Towards Using Data-Driven Approaches in Personalised Long-Term Interactions

Coffee With a Hint of Data: Towards Using Data-Driven Approaches in Personalised Long-Term Interactions

Demonstration of a Robo- Barista for In the Wild Interactions

Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination

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