Motivated by the expectation that robot presence at crime scenes will become increasingly prevalent, the question arises of how they can protect humans in their care or vicinity. The current paper delves into the concept of “robot self-defense” and explores whether a robot should be tele-operated or autonomous, and how humans perceive imperfections in robot performance. To gain insight into how people feel, an online survey was conducted with 180 participants, who watched six videos of a robot defending a victim. The study provides insights into trust in human-robot interactions and sheds light on the complex dynamics involved in robot self-defense. The results indicate that people found a tele-operated robot to be more accepted, and that attempting to help but failing is more acceptable than just observing.

Would it be okay for a robot to hurt a human, if by doing so it could protect someone else? Such ethical questions could be vital to consider, as the market for social robots grows larger and robots become increasingly prevalent in our surroundings. Here we introduce the topic of “robot self-defense”, which involves the use of force by a robot in response to violence, to protect a human in its care. To explore this topic, we conducted a preliminary analysis of the literature, as well as brainstorming sessions, which led us to formulate an idea about how people will perceive robot self-defense based on the perceived risk of loss. Additionally, we propose a study design to investigate how the general public will perceive the acceptability of a robot using self-defense techniques. As part of this, we describe some hypotheses based on the assumption that the perceived acceptability will be affected by both the entities involved in a violent situation and the amount of force that is applied. The proposed scenarios will be used in a future survey to evaluate participants' perception of a social robot using self-defense techniques under varying circumstances, toward stimulating ideation and discussion on how robots will be able to help people to live better lives.

Robots are being designed to communicate with people in various public and domestic venues in a helpful, discreet way. Here, we use a speculative approach to shine light on a new concept of robot steganography (RS), that a robot could seek to help vulnerable populations by discreetly warning of potential threats. We first identify some potentially useful scenarios for RS related to safety and security -- concerns that are estimated to cost the world trillions of dollars each year -- with a focus on two kinds of robots, an autonomous vehicle (AV) and a socially assistive humanoid robot (SAR). Next, we propose that existing, powerful, computer-based steganography (CS) approaches can be adopted with little effort in new contexts (SARs), while also pointing out potential benefits of human-like steganography (HS): although less efficient and robust than CS, HS represents a currently-unused form of RS that could also be used to avoid requiring computers or detection by more technically advanced adversaries. This analysis also introduces some unique challenges of RS that arise from message generation, indirect perception, and effects of perspective. For this, we explore some related theoretical and practical concerns for selecting carrier signals and generating messages, also making available some code and a video demo. Finally, we report on checking the current feasibility of the RS concept via a simplified user study, confirming that messages can be hidden in a robot's behaviors. The immediate implication is that RS could help to improve people's lives and mitigate some costly problems -- suggesting the usefulness of further discussion, ideation, and consideration by designers.

Could a social robot use force to prevent violence directed toward humans in its care?–Might crime be eradicated, or conversely could excessive use of force proliferate and human dignity become trampled beneath cold robotic wheels? Such speculation is one part of a larger, increasingly important question of how social robots will be expected to behave in our societies, as robotic technologies develop and become increasingly widespread. Here, to gain some insight into this topic of "robot self-defense", we proposed a simplified heuristic based on perceived risk of loss to predict acceptability, and conducted a user survey with 304 participants, who watched eight animated videos of robots and humans in a violent altercation. The results indicated that people largely accept the idea that a humanoid robot can use force on attackers to help others. Furthermore, self-defense was perceived as more acceptable when the appearance of the defender was humanoid rather than mechanical, and when the force disparity between attacker and defender was high. The immediate suggestion is that it could be beneficial to re-examine common assumptions that a robot should never harm or risk harming humans, and to discuss and consider the possibilities for robot self-defense.

In this paper, we conceptualise and propose integrating UAVs with Intelligent Transportation Systems (ITS) based on using the Cooperative-ITS (C-ITS) framework. We start by discussing the state of the art and pinpointing some of the reasons for integration and the applications that the envisaged integration would enable. Next, we recall the critical aspects of the state of the art C-ITS connectivity and discuss how seamless integration of UAVs into C-ITS can be achieved. Notably, we show that encapsulation of UAVs in C-ITS does not imply significant changes for the currently existing mechanisms and data formats. Finally, we discuss some of the open research challenges related to the integration and operation of the integrated systems and pinpoint some mechanisms which can help to address these.

Autonomous vehicles (AVs) are crucial robotic systems that promise to improve our lives via safe, efficient, and inclusive transport–while posing some new challenges for the education of future researchers in the area, that our current research and education might not be ready to deal with: In particular, we don’t know what the AVs of the future will look like, practical learning is restricted due to cost and safety concerns, and a high degree of multidisciplinary knowledge is required. Here, following the broad outline of Active Student Participation theory, we propose a pedagogical approach targeted toward AVs called CAR that combines Creativity theory, Applied demo-oriented learning, and Real world research context. Furthermore, we report on applying the approach to stimulate learning and engagement in a master’s course, in which students freely created a demo with 10 small robots running ROS2 and Ubuntu on Raspberry Pis, in connection to an ongoing research project and a real current problem (SafeSmart and COVID-19). The results suggested the feasibility of the CAR approach for enabling learning, as well as mutual benefits for both the students and researchers involved, and indicated some possibilities for future improvement, toward more effective integration of research experiences into second cycle courses.

Socially assistive robots are increasingly being designed to interact with humans in various therapeutical scenarios. We believe that one useful scenario is providing exercise coaching for Persons with Dementia (PWD), which involves unique challenges related to memory and communication. We present a design for a robot that can seek to help a PWD to conduct exercises by recognizing their behaviors and providing appropriate feedback, in an online, multimodal, and engaging way. Additionally, following a mid-fidelity prototyping approach, we report on some observations from an exploratory user study using a Baxter robot; although limited by the sample size and our simplified approach, the results suggested the usefulness of the general scenario, and that the degree to which a robot provides feedback-occasional or continuous- could moderate impressions of attentiveness or fun. Some possibilities for future improvement are outlined, touching on richer recognition and behavior generation strategies based on deep learning and haptic feedback, toward informing next designs.

Activity recognition, involving a capability to recognize people's behavior and its underlying significance, will play a crucial role in facilitating the integration of interactive robotic artifacts into everyday human environments. In particular, social intelligence in recognizing affectionate behavior will offer value by allowing companion robots to bond meaningfully with interacting persons. The current article addresses the issue of designing an affectionate haptic interaction between a person and a companion robot by exploring how a small humanoid robot can behave to elicit affection while recognizing touches. We report on an experiment conducted to gain insight into how people perceive three fundamental interactive strategies in which a robot is either always highly affectionate, appropriately affectionate, or superficially unaffectionate (emphasizing positivity, contingency, and challenge, respectively). Results provide insight into the structure of affectionate interaction between humans and humanoid robots—underlining the importance of an interaction design expressing sincere liking, stability and variation—and suggest the usefulness of novel modalities such as warmth and cold.

In order to facilitate integration into domestic and public environments, companion robots can seek to communicate in a familiar, “socially intelligent” manner, recognizing typical behaviors which people direct toward them. One important type of behavior to recognize is the displaying and seeking of affection, which is fundamentally associated with the modality of touch. This paper identifies how people communicate affection through touching a humanoid robot appearance, and reports on the development of a recognition system exploring the modalities of touch and vision. Results of evaluation indicate the proposed system can recognize people's affectionate behavior in the designated context.

This research constitutes an initial investigation into key issues which arise in designing a flying humanoid robot (FHR), with a focus on human-robot interaction (HRI). The humanoid form offers an interface for natural communication; flight offers excellent mobility. Combining both will yield companion robots capable of approaching, accompanying, and communicating naturally with humans in difficult environments. Problematic is how such a robot should best fly around humans, and what effect a robot's flight will have on a person in terms of non-verbal communicative cues. To answer these questions, we propose an extension to existing proxemics theory (“z-proxemics”) and predict how typical humanoid flight motions will be perceived (“z-kinesics”). Data obtained from participants watching animated sequences are analyzed to check our predictions. The paper also reports on the building of a flying humanoid robot, which we will use in interactions.

People like to play, and robotic technology offers the opportunity to interact with artifacts in new ways. Robots co-existing with humans in domestic and public environments are expected to behave as companions, also engaging in playful interaction. If a robot is small, we foresee that people will want to be able to pick it up and express their intentions playfully by hugging, shaking and moving it around in various ways. Such robots will need to recognize these gestures--which we call “full-body gestures” because they affect the robot's full body. Inertial sensors inside the robot could be used to detect these gestures, in order to avoid having to rely on external sensors in the environment. However, it is not obvious which gestures typically occur during play, and which of these can be reliably detected. We therefore investigate full-body gesture recognition using Sponge Robot, a small humanoid robot equipped with inertial sensors and designed for playful human-robot interaction.