A Mind-inspired Architecture for Adaptive HRI

Umbrico, Alessandro; Benedictis, Riccardo De; Fracasso, Francesca; Cesta, Amedeo; Orlandini, Andrea; Cortellessa, Gabriella

doi:10.1007/s12369-022-00897-8

Cited by 7 publications

(5 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cross-cultural interactions and the efficacy of robot greetings and gestures were examined in a particular context [41,42]. Researchers aspired to devise empathetic and multi-modal interactions grounded in the emotional-cognitive profiles of users [43].…”

Section: Main Research Aimsmentioning

confidence: 99%

“…[45] and [51] employed pre-test and post-test questionnaires. In [43] the participants were requested to complete the Chat-bot Usability Questionnaire. In [28] the length of human speech was measured through questionnaires, while in [58] data was gathered using TIPI and RoSAS questionnaires.…”

Section: Data Collectionmentioning

confidence: 99%

“…Transparency is vital for ensuring that the robot's behavior aligns with participants' expectations. In [43] the importance of balancing transparency and legibility of the robot's goals were highlighted, emphasizing that a clear understanding of the robot's intentions contributes to successful interactions.…”

Section: Sociability and Familiaritymentioning

confidence: 99%

See 2 more Smart Citations

Nonverbal Behavior of Service Robots in Social Interactions – A Survey on Recent Studies

Leoste,

Marmor,

Heidmets

2024

Preprint

View full text Add to dashboard Cite

This study presents a literature review focused on nonverbal communication in human-robot interaction (HRI) that involves service robots with social capabilities. We aim to list the types of robots used and nonverbal communication cues examined in the reviewed studies; and the main research objectives, participant characteristics, data collection methods, and primary findings of these studies. To achieve this, we conducted a literature review of 39 relevant open access academic papers published from 2006 to 2023 that examined the utilization of nonverbal cues by both humans and robots during HRI. The results suggest that enhancing the quality of communication between humans and service robots must be improved, while there are several aspects that require more thorough exploring, needed to strengthen robot self-efficacy, trust and trustworthiness in HRI or overcome cultural differences. The results emphasize the importance of nonverbal communication in shaping the dynamics of interactions between humans and service robots.

show abstract

Section: Main Research Aimsmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

Nonverbal Behavior of Service Robots in Social Interactions – A Survey on Recent Studies

Leoste,

Marmor,

Heidmets

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The robot was able to infer human intentional and emotional states during shared plan execution and adapt to changes of such internal states in order to behave appropriately. Umbrico et al [23] introduced a cognitive architecture dedicated to supporting the intelligent and adaptive behavior of social robots. The scenario was a training service where the robot is used for delivering a cognitive rehabilitation program to different patients, characterized by different cognitive profiles, and assessed by a clinician.…”

Section: Adaptivity In Hrimentioning

confidence: 99%

Redefining User Expectations: The Impact of Adjustable Social Autonomy in Human–Robot Interaction

Cantucci,

Falcone,

Marini

2023

Electronics

View full text Add to dashboard Cite

To promote the acceptance of robots in society, it is crucial to design systems exhibiting adaptive behavior. This is particularly needed in various social domains (e.g., cultural heritage, healthcare, education). Despite significant advancements in adaptability within Human-Robot Interaction and Social Robotics, research in these fields has overlooked the essential task of analyzing the robot’s cognitive processes and their implications for intelligent interaction (e.g., adaptive behavior, personalization). This study investigates human users’ satisfaction when interacting with a robot whose decision-making process is guided by a computational cognitive model integrating the principles of adjustable social autonomy. We designed a within-subjects experimental study in the domain of Cultural Heritage, where users (e.g., museum visitors) interacted with the humanoid robot Nao. The robot’s task was to provide the user with a museum exhibition to visit. The robot adopted the delegated task by exerting some degree of discretion, which required different levels of autonomy in the task adoption, relying on its capability to have a theory of mind. The results indicated that as the robot’s level of autonomy in task adoption increased, user satisfaction with the robot decreased, whereas their satisfaction with the tour itself improved. Results highlight the potential of adjustable social autonomy as a paradigm for developing autonomous adaptive social robots that can improve user experiences in multiple HRI real domains.

show abstract

“…The final category, hybrid cognitive architectures, includes popular models like CARACaS [200], [201]; iCub [202], MIRIAM [203] and IMPACT [204]. They incorporate both symbolic and connectionist capabilities in their design to facilitate learning and adaptation, as well as reasoning over acquired knowledge.…”

mentioning

confidence: 99%

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Mumuni

2022

Prog Artif Intell

View full text Add to dashboard Cite

We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-shot learning. Data-driven deep learning models have achieved remarkable performance and demonstrated capabilities surpassing human experts in many applications. Yet, their inability to exploit domain knowledge leads to serious performance limitations in practical applications. In particular, deep learning systems are exposed to adversarial attacks, which can trick them into making glaringly incorrect decisions. Moreover, complex data-driven models typically lack interpretability or explainability, i.e., their decisions cannot be understood by human subjects. Furthermore, models are usually trained on standard datasets with a closed-world assumption. Hence, they struggle to generalize to unseen cases during inference in practical open-world environments, thus, raising the zero-or few-shot generalization problem. Although many conventional solutions exist, explicit domain knowledge, brain-inspired neural network and cognitive architectures offer powerful new dimensions towards alleviating these problems. Prior knowledge is represented in appropriate forms and incorporated in deep learning frameworks to improve performance. Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots. Ultimately, these models achieve better explainability, higher adversarial robustness and data-efficient learning, and can, in turn, provide insights for cognitive science and neurosciencethat is, to deepen human understanding on how the brain works in general, and how it handles these problems.Index Terms-Domain knowledge, cognitive architecture, brain-inspired neural network, explainable AI, adversarial attack, zero-shot generalization. ✦ INTRODUCTION State of deep learningMachine learning is an artificial intelligence (AI) concept that enables computing systems to learn useful relationships from data and then use this information to identify learned patterns and make predictions on new inputs. Deep learning (DL) is a machine learning method that uses a multi-layered arrangement of computational units to learn relevant patterns from data. Many deep learning constructs have been proposed for various use-cases. Some of the most popular ones are deep convolutional neural networks (DCNN) [1], [2] recurrent neural networks (RNN) [3], generative adversarial networks (GANs) [4], deep reinforcement learning [5] and vision transformers [6], [7]). DCNNs are a class of deep neural networks (DNNs) specially designed to handle image data.Recently, deep learning methods have accomplished remarkable milestones in many critical applications. In medical domains, some state-of-the-art models (e.g., as reported

show abstract

A Mind-inspired Architecture for Adaptive HRI

Cited by 7 publications

References 55 publications

Nonverbal Behavior of Service Robots in Social Interactions – A Survey on Recent Studies

Nonverbal Behavior of Service Robots in Social Interactions – A Survey on Recent Studies

Redefining User Expectations: The Impact of Adjustable Social Autonomy in Human–Robot Interaction

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Contact Info

Product

Resources

About