Thanks to the recent advancement of robotic solutions and computational intelligence, autonomous robots that interact with humans are nowadays becoming more available to the public, offering different benefits to support people in various organisational contexts, such as education, assistive applications, customer service, and home maintenance. These robots are envisioned to deliver meaningful benefits through efficient and effective interaction with humans to fulfil their expectations. These beneficial effects, however, may not always be realised due to maladaptive forms of interaction. To establish a successful human–robot interaction (HRI), besides the perceptual and cognitive capabilities, an autonomous robot should be able to adapt its behaviour in real time and often in partially unknown environments, make necessary adjustments to the situation at hand, and, in turn, achieve the high-level goal, e.g., assisting a person to solve a puzzle or a tricky maths problem.
In contrast to automation that follows pre-programmed “rules” and is limited to specific actions, autonomous robots are envisioned to have a context-guided behaviour adaptation capability, which would allow them to have a degree of self-governance, enabling them to learn and respond actively to situations that were not pre-programmed by the developer. Although this capability of the robot would potentially promote HRIs, there are serious concerns regarding the impact of technology adaptation on human trust, as the actions of the robots involved in HRI will become less predictable. Thus, it is believed that a successful and trustworthy HRI must be a trade-off between the robot’s behaviour adaptation capability and the robot’s capability in measuring and manipulating other community- and individual-relevant factors such as trust, where the human is the trustor and the robot is the trustee, with the final aim of maximising the outcomes of the HRI.
This Special Issue aims to cover different aspects of the recent advances in the human–robot interaction field, including the development of architectures and modules for sensing, cognition, and control of robotic systems involved in HRIs, user studies, analysis, assessment and validation of robotic systems, as well as work in progress in this field. Authors are encouraged to submit both original research articles and surveys. Research articles should address the originality, as well as practical aspects and implementation, of the work in the field, while surveys should provide an overview and up-to-date information.
We welcome submissions from all topics of HRI applied to industry, health, and education, including, but not limited to, the following topics:
Development of robotic frameworks for sense, cognition, and control;
Context-guided behaviour adaptation in HRIs;
Mutual perception in closed-loop HRIs;
Contextual reasoning in HRIs;
Learning by demonstration;
Human factors in HCI/HRI;
Human-guided reinforcement learning;
Interpretable machine learning with human-in-the-loop;
Trust and autonomy in HRIs in different contexts;
Trust measurement tools in HRIs;
HRIs in real-world settings.
Dr. Abolfazl Zaraki
Dr. Hamed Rahimi Nohooji