Social-emotional skills, such as recognizing, under-standing, and regulating one's emotions and those of others, along with cognitive skills like working memory, phonological aware-ness, and grapheme-phoneme correspondence, are highly valued in developing cognitive, social, and emotional competencies. During the past 10 years, there has been an increasing interest in research on social robots as teaching assistants, peers, animators, or even teachers, as numerous studies have demonstrated their beneficial impact on student social skills. The physical design and advanced capabilities of humanoid robots like SoftBank Robotics' NAO demonstrate that they represent cutting-edge technology with a wide range of functionalities, enhancing the learning experience during Child-Robot Interaction (CRI). In this work, we have designed and implemented fourteen (14) educational applications for the NAO robot, evaluating its pedagogical impact in real school settings. Specifically, nine (9) focused on emotional competencies, one (1) on social interaction and collaboration, three (3) on working memory, and one (1) on the development of phonological awareness and grapheme-phoneme correspondence. For validation and evaluation, qualitative research was carried out in kindergartens in Attica, involving 42 preschool-aged students (4–6 years) in pre- / post-test studies. Measurements were made through observation and discussion, using video recordings, evaluation / self-evaluation sheets, rubrics, and statistical analysis. Our results and findings show that the children exhibited high participation in communicating their emotions to NAO, or even imitating it, conveying various emotions through facial expres-sions, gestures and body language. The children adapted their emotional expression according to the activity guidelines, helping NAO by proposing solutions to emotional regulation (sadness) or collaborating to solve common problems. Finally, it appears that the NAO robot can enhance both working memory and phonological awareness. Our future steps involve more long-term research in a wider audience and “humanising” NAO through AI integration to provide more physical human interactions.

Human-like social robots (or humanoids) such as Softbank's NAO6, have been proven valuable assistants, able to advance State-of-the-Art of Technology in Education and Learning (TEL) as they are quite impressive “clones” of human behavior, and due to their relatable form, are often perceived as superior social companions. The rise of accessible Large Language Models and cloud computing, could transform robots like NAO6- a rather obsolete robot with quite low computing capacity (Pentium CPU, 2–4 GB RAM)- into a capable social agent, able to adopt A.I. behavior. In the current paper, we present a solution to enrich Softbank NAO6with A.I. capacity, in order to act as an LLM vessel. Specifically, we managed to connect an augmented AI chatbot to NAO6by deploying corresponding Python APIs. In our showcase, a NAO6acts as the ancient Greek Philosopher Plato that “guides the one who seeks wisdom” based on his theory. Our solution has been evaluated in real crowded settings as a proof-of-concept. Next steps involve to evaluate our solution in school classrooms.

This paper reports on insights by robotics researchers that participated in a 5-day robot-assisted nuclear disaster response field exercise conducted by Kerntechnische Hilfdienst GmbH (KHG) in Karlsruhe, Germany. The German nuclear industry established KHG to provide a robot-assisted emergency response capability for nuclear accidents. We present a systematic description of the equipment used; the robot operators' training program; the field exercise and robot tasks; and the protocols followed during the exercise. Additionally, we provide insights and suggestions for advancing disaster response robotics based on these observations. Specifically, the main degradation in performance comes from the cognitive and attentional demands on the operator. Furthermore, robotic platforms and modules should aim to be robust and reliable in addition to their ease of use. Last, as emergency response stakeholders are often skeptical about using autonomous systems, we suggest adopting a variable autonomy paradigm to integrate autonomous robotic capabilities with the human-in-the-loop gradually. This middle ground between teleoperation and autonomy can increase end-user acceptance while directly alleviating some of the operator's robot control burden and maintaining the resilience of the human-in-the-loop.

This paper addresses the problem of the human operator cognitive workload estimation while controlling a robot. Being capable of assessing, in real-time, the operator’s workload could help prevent calamitous events from occurring. This workload estimation could enable an AI to make informed decisions to assist or advise the operator, in an advanced human-robot interaction framework. We propose a method, named Fessonia, for real-time cognitive workload estimation from multiple parameters of an operator’s driving behaviour via the use of behavioural entropy. Fessonia is comprised of: a method to calculate the entropy (i.e. unpredictability) of the operator driving behaviour profile; the Driver Profile Update algorithm which adapts the entropy calculations to the evolving driving profile of individual operators; and a Warning And Indication System that uses workload estimations to issue advice to the operator. Fessonia is evaluated in a robot teleoperation scenario that incorporated cognitively demanding secondary tasks to induce varying degrees of workload. The results demonstrate the ability of Fessonia to estimate different levels of imposed workload. Additionally, it is demonstrated that our approach is able to detect and adapt to the evolving driving profile of the different operators. Lastly, based on data obtained, a decrease in entropy is observed when a warning indication is issued, suggesting a more attentive approach focused on the primary navigation task.

This paper addresses the problem of human operator intent recognition during teleoperated robot navigation. In this context, recognition of the operator’s intended navigational goal, could enable an artificial intelligence (AI) agent to assist the operator in an advanced human-robot interaction framework. We propose a Bayesian Operator Intent Recognition (BOIR) probabilistic method that utilizes: (i) an observation model that fuses information as a weighting combination of multiple observation sources providing geometric information; (ii) a transition model that indicates the evolution of the state; and (iii) an action model, the Active Intent Recognition Model (AIRM), that enables the operator to communicate their explicit intent asynchronously. The proposed method is evaluated in an experiment where operators controlling a remote mobile robot are tasked with navigation and exploration under various scenarios with different map and obstacle layouts. Results demonstrate that BOIR outperforms two related methods from literature in terms of accuracy and uncertainty of the intent recognition.