Human-Robot Interaction (HRI) research in real-world settings may lead to unanticipated, emotionally charged moments. While impacts of these moments on participants are reported in the literature, researchers’ emotions, which can affect participants’ experiences, are often left unreported. Learning from these moments is essential for advancing HRI quality and real-world deployment success. We introduce "Emotional Entanglements" as a lens in HRI to define a researcher's capacity to anticipate, absorb, respond to, and recover from emotionally impactful events. Collecting testimonials using collaborative autoethnography from eleven researchers, we surface recurring emotional entanglements experienced in HRI studies, including tears with mixed meaning, participant attachment and loss upon robot withdrawal, and consequential participant decisions attributed to the robot, as well as how researchers navigated them amidst protocol constraints. This paper provides an actionable guide to "Emotional Sustainability in HRI", raising awareness of these often unreported situations and offering strategies for mitigation.

Figure 1: The tapestry employed a user-centred approach in its design and implementation.We developed the visuals and crafted elements to support the narrative plot about marine noise pollution.The experience also incorporates tangible interfaces, audio and sensors to engage the users and sensitise them to this topic.

Our ancestors communicated stories through tapestries, using them to adorn both public and private spaces. Traditionally, these tapestries were static artworks hanging on walls without any interaction from the audience. Nevertheless, textiles offer a versatile medium, which can be crafted from various materials and colours and manipulated to produce unique, touch-responsive textures. Our vision is to integrate the tactile capabilities of weaving with capacitive sensor technology to create a media-immersed interactive art installation that explores the negative impact of noise pollution on marine life ecosystems. Marine animals, particularly cetaceans, heavily rely on sound for communication, navigation, and hunting, and noise pollution can disrupt these essential functions. By harnessing the storytelling potential of tapestries and the power of capacitive sensors connected to microcontrollers, we developed a storytelling experience with a unique embodied interface that aims to educate the general audience about pressing issues such as marine noise pollution.

Robot-Enhanced Therapies (RET) offer a promising alternative for Autism Spectrum Disorder (ASD) children. Within this framework, an imitation therapy is proposed where children replicate gestures demonstrated by a robot, including those derived from popular children songs. To enable effective feedback provision by the robot, gesture recognition becomes paramount. This paper introduces two approaches for achieving this goal: a multiclass classifier, intended to recognize which gesture is executed, and a set of binary classifiers discerning whether the expected gesture is performed or not. Both models rely on kinematic data acquired through the Azure Kinect camera and a Residual Network as classification model. Moreover, considering the challenges in children’s data collection for model training, the work explores the impact that the collection of their data can bring to the outcomes of a gesture classification algorithm. Beside testing on adults and healthy children datasets, the study leverages a dataset comprising 69 gestures performed by ASD children during therapy sessions. Results indicate that binary models trained also on children data outperform the multiclass approach trained solely on adults data, achieving a median accuracy of 86%. This underscores the effectiveness of binary classifiers and suggests that integrating children’s data can enhance algorithm performance. To strengthen findings, future research should expand dataset size, especially considering ASD children, and explore alternative action recognition algorithms like Long Short-Term Memories (LSTM).