This paper argues that aesthetics in Human–Robot Interaction (HRI) should be understood as an integrated system of perception, emotion, and meaning making, rather than as surface-level styling. A concise review connects aesthetic theory, design studies, and branding research to show how form, material, motion, and context shape user judgement, trust, and desire. This review also highlights the prevailing tendencies in robotics research to focus on isolated visual features, while often overlooking the contextual and symbolic dimensions that influence user interpretation in their daily lives.

Robots should not make us feel uncomfortable in our own homes, and so we must be able to trust them. Proactive robot assistants, designed to collaborate with humans on Activities of Daily Living (ADLs), operate in a tricky social situation as they emulate complex human-human interactions. This paper considers how humans expect robots to interact in such situations, particularly with regard to action confirmations, and how this impacts trust. A video-based user study was conducted with over 100 participants, comparing three distinct interaction personas. Our findings highlight that: (i) while communication and explanation are a significant factor in improving trust, it may be necessary in many cases to actually confirm actions before executing them; and (ii) there is a divide between individuals who value efficiency and speed versus those who put control above all else.

While Human Activity Recognition systems may benefit from Active Learning by allowing users to self-annotate their Activities of Daily Living (ADLs), many proposed methods for collecting such annotations are for short-term data collection campaigns for specific datasets. We present a reusable dialogue-based approach to user interaction for active learning in activity recognition systems, which utilises semantic similarity measures and a dataset of natural language descriptions of common activities (which we make publicly available). Our approach involves system-initiated dialogue, including follow-up questions to reduce ambiguity in user responses where appropriate. We apply this approach to two active learning scenarios: (i) using an existing CASAS dataset, demonstrating long-term usage; and (ii) using an online activity recognition system, which tackles the issue of online segmentation and labelling. We demonstrate our work in context, in which a natural language interface provides knowledge that can help interpret other multi-modal sensor data. We provide results highlighting the potential of our dialogue- and semantic similarity-based approach. We evaluate our work: (i) quantitatively, as an efficient way to seek users’ input for active learning of ADLs; and (ii) qualitatively, through a user study in which users were asked to compare our approach and an established method. Results show the potential of our approach as a hands-free interface for annotation of sensor data as part of an active learning system. We provide insights into the challenges of active learning for activity recognition under real-world conditions and identify potential ways to address them.

While Human Activity Recognition (HAR) systems may benefit from Active Learning (AL) by allowing users to self-annotate their Activities of Daily Living (ADLs), many proposed methods for collecting such annotations are for short-term data collection campaigns for specific datasets. We present a reusable dialogue-based approach to user interaction for active learning in HAR systems, which utilises a dataset of natural language descriptions of common activities (which we make publicly available) and semantic similarity measures. Our approach involves system-initiated dialogue, including follow-up questions to reduce ambiguity in user responses where appropriate. We apply our work to an existing CASAS dataset in an active learning scenario, to demonstrate our work in context, in which a natural language interface provides knowledge that can help interpret other multi-modal sensor data. We provide results highlighting the potential of our dialogue- and semantic similarity-based approach. We evaluate our work: (i) technically, as an effective way to seek users’ input for active learning of ADLs; and (ii) qualitatively, through a user study in which users were asked to use our approach and an established method, and to subsequently compare the two. Results show the potential of our approach as a user-friendly mechanism for annotation of sensor data as part of an active learning system.

Abstract Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. To reduce the amount of preparation and preprogramming required for their deployment in real‐world applications, it is important to make these systems self‐adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre‐existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to proactively and smoothly adapt to subtle changes in the environment and in the habits and preferences of their user(s), in presence of appropriately defined performance measuring functions.

Mobile Crowd Sensing (MCS) is an emerging paradigm that exploits the ubiquity of smartphones and cheap sensor devices to collect data and thus contribute to the provision of useful services, especially in the domains of urban life. While many MCS implementations have been proposed for different applications, the lack of common performance metrics means that their efficiency cannot be easily compared. In this paper, we formalize a generic coverage model for the class of MCS systems sampling spatial phenomena before introducing a way to produce one such a metric by exploiting a spatio-temporal estimator. We avail of a large-scale dataset of users' mobility traces to demonstrate the use of the newly introduced metric in informing the resolution of a typical problem in MCS system design.

In order to inform the design of robotic applications for children, in this paper we describe and report the results of an experiment we conducted in a primary school. Our work investigates the effects of the robot's perceived gender and age on levels of engagement and acceptance of the robot by children across different age and gender groups. Our results show that children across ages relate differently toward perceived robot's age and gender.

Service robots employed in public spaces need to be equipped with specific sensing, reasoning and human-robot interaction capabilities to adapt their interaction style and thus effectively engage with a variety of users. In this paper we present a method used by an ubiquitous robotic system to gather 3D body metrics and use them to robustly estimate age and gender of previously unseen participants in real-world multi-party situations. We evaluate system's performance on 428 children volunteers and compare them with those obtainable with a state of the art software based on face analysis. This work demonstrates that even small number of biometrics can achieve good age and gender estimation results in perceptually challenging environments. Finally, this paper illustrates how the system is used to inform the online adaptation of the behavior of a humanoid robot.

Networked robotic applications enable robots to operate in distant, hazardous, or otherwise inaccessible environments, such as search and rescue, surveillance, and exploration applications.

Addressing the obesity epidemic that plagues many societies remains an outstanding public health issue. One innovative approach to addressing this problem is Exergaming. A combination of "Exercise" and "Gaming", the objective is to motivate people participate in exercise regimes, usually in their home environment. In this paper a more holistic interpretation of this exercise paradigm is proposed. Freegaming augments Exergaming in a number of key dimensions but especially through the promotion of games in outdoor mobile contexts and within a social environment. The design and implementation of a platform for Freegaming is described and illustrated through the description of a sample game.

Endowing robots with a social interface is often costly and difficult. Virtual characters on the other hand are comparatively cheap and well equipped but suffer from other difficulties, most notably their inability to interact with the physical world. This paper details our wearable solution to combining physical robots and virtual characters into a mixed reality agent (MiRA) through mixed reality visualisation. It describes a pilot study demonstrating our system, and showing how such a technique can offer a viable alternative cost effective approach to enabling a rich social interface for human-robot interaction.

This paper contests that Mixed Reality (MR) offers a potential solution in achieving transferability between Human Computer Interaction (HCI) and Human Robot Interaction (HRI). Virtual characters (possibly of a robotic genre) can offer highly expressive interfaces that are as convincing as a human, are comparably cheap and can be easily adapted and personalized. We introduce the notion of a mixed reality agent, i.e. an agent consisting of a physical robotic body and a virtual avatar displayed upon it. We realized an augmented reality interface with a Head-Mounted Display (HMD) in order to interact with such systems and conducted a pilot study to demonstrate the usefulness of mixed reality agents in human-robot collaborative tasks.