Telepresence robots have the potential to change our experiences in galleries and museums, allowing for a range of hybrid interactions for visitors and museum professionals, improving accessibility, offering activities or information, and providing a range of practical use cases (e.g. the robots augmenting museum exhibits).We present the results of 3 qualitative studies conducted in the UK exploring the acceptability (1 -interviews with museum professionals with no previous exposure to telepresence), acceptance (2 -focus groups for initial exposure to telepresence robots), and adoption (3 -interviews with museum professionals with long-term exposure to

Friction – disagreement and breakdown – is an omnipresent aspect of conducting interdisciplinary research yet is rarely presented in formal research reporting. We analyse a performance-led research process where professional dancers with different disabilities explored how to improvise with an industrial robot, with the support of an interdisciplinary team of human-computer and human-robot interaction researchers. We focus on one site of friction in our research process; how to dance – safely – with robots? By presenting our research process, we exemplify the different ways in which we encountered this friction and how we reconfigured the research process around it. We contribute five ways in which we arrived at a generative ethical outcome, which may be helpful in productively engaging with friction in interdisciplinary collaboration.

Glitches – moments when technologies do not work as desired – will become increasingly common as industrially-designed robots move into complex contexts. Taking glitches to be potential sites of critical ethical reflection, we examine a glitch that occurred in the context of a collaborative research project where professional dancers with different disabilities improvised with a robotic arm. Through a first-person account, we analyse how the dancer, the robot, and the rest of the research team enacted ethics in the moment of glitch. Through this analysis, we discovered a deep and implicit ethical misalignment wherein our enactments of ethics in response to the glitch did not align with the values of the project. This prompted a critical re-engagement with our research process through which we forged a dialogue between different ethical perspectives that acted as an invitation to bring us back into ethical alignment with the project’s values.

As robots enter the messy human world so the vital matter of safety takes on a fresh complexion with physical contact becoming inevitable and even desirable. We report on an artistic-exploration of how dancers, working as part of a multidisciplinary team, engaged in contact improvisation exer-cises to explore the opportunities and challenges of dancing with cobots. We reveal how they employed their honed bodily senses and physical skills to engage with the robots aesthetically and yet safely, interleaving improvised physical manipulations with reflections to grow their knowledge of how the robots behaved and felt. We introduce somatic safety, a holistic mind-body approach in which safety is learned, felt and enacted through bodily contact with robots in addition to being reasoned about. We conclude that robots need to be better designed for people to hold them and might recognise tacit safety cues among people. We propose that safety should be learned through iterative bodily experience interleaved with reflection.

While ethical challenges are widely discussed in HCI, far less is reported about the ethical processes that researchers routinely navigate. We reflect on a multispecies project that negotiated an especially complex ethical approval process. Cat Royale was an artist-led exploration of creating an artwork to engage audiences in exploring trust in autonomous systems. The artwork took the form of a robot that played with three cats. Gaining ethical approval required an extensive dialogue with three Institutional Review Boards (IRBs) covering computer science, veterinary science and animal welfare, raising tensions around the welfare of the cats, perceived benefits and appropriate methods, and reputational risk to the University. To reveal these tensions we introduce beneficiary-epistemology space, that makes explicit who benefits from research (humans or animals) and underlying epistemologies. Positioning projects and IRBs in this space can help clarify tensions and highlight opportunities to recruit additional expertise.

We reflect on the design of a multispecies world centred around a bespoke enclosure in which three cats and a robot arm coexist for six hours a day during a twelve-day installation as part of an artist-led project. In this paper, we present the project’s design process, encompassing various interconnected components, including the cats, the robot and its autonomous systems, the custom end-effectors and robot attachments, the diverse roles of the humans-in-the-loop, and the custom-designed enclosure. Subsequently, we provide a detailed account of key moments during the deployment and discuss the design implications for future multispecies systems. Specifically, we argue that designing the technology and its interactions is not sufficient, but that it is equally important to consider the design of the ‘world’ in which the technology operates. Finally, we highlight the necessity of human involvement in areas such as breakdown recovery, animal welfare, and their role as audience.

A large number of deep learning based object detection algorithms have been proposed and applied in a wide range of domains such as security, autonomous driving and robotics. In practical usage, objects being occluded are common, and can result in reduced accuracy and reliability. To increase the robustness of object detection algorithms under occlusion scenarios, it is necessary to consider the influence of different types of occlusion on the performance of object detection approaches. Our research revealed a gap in benchmarking datasets that could provide exemplars of occlusion that covered a range of occlusion scenarios. In this paper, we present a new benchmarking dataset that includes a range of exemplars providing coverage of different types of occlusion cases. This dataset is designed for object detection of everyday objects in indoor scenarios, and comprises occlusion in three orthogonal atomic factors, namely, the degree of occlusion, the location of occlusion, and classes of occluded object and those occluding other objects. Our dataset is balanced in terms of classes and degrees of occlusion, with a total of 5970 sample images. The effect of these three atomic factors has been investigated on some classic general object detectors. Using this benchmarking dataset, we also present results on the impact of the distribution of the training dataset, in terms of degree of occlusion, on the robustness of several typical object detection algorithms (e.g. Fast RCNN, Faster RCNN, and FCOS, etc). The benchmark is available at “https://drive.google.com/drive/folders/13VkLgbx6t0-vA3vRWrlvHcjra-8BS4aL?usp=sharing”. This dataset is seen as a key contribution to research investigating the influence of occlusion on the performance of object detectors.