The use of motion capture in live dance performances has created an emerging discipline enabling dancers to play different avatars on the digital stage. Unlike classical workflows, avatars enable performers to act as different characters in customized narratives, but research has yet to address how movement, improvisation, and perception change when dancers act as avatars. We created five avatars representing differing genders, shapes, and body limitations, and invited 15 dancers to improvise with each in practice and performance settings. Results show that dancers used avatars to distance themselves from their own habitual movements, exploring new ways of moving through differing physical constraints. Dancers explored using gender-stereotyped movements like powerful or feminine actions, experimenting with gender identity. However, focusing on avatars can coincide with a lack of continuity in improvisation. This work shows how emerging practices with performance technology enable dancers to improvise with new constraints, stepping outside the classical stage.

The body condition scoring (BCS) of pigs comprehensively reflects the health status of pigs by evaluating their fat and muscle reserves. Traditional evaluation methods rely on manual measurement of pig backfat thickness, which is inefficient and can causes severe stress reactions in pigs. In recent years, the development of computer vision has provided new ideas for pig BCS evaluation. This paper proposes a pig body condition score evaluation method based on the MobileSAM. The method first detects pigs from a rear-view perspective, then uses the resulting bounding boxes as prompts for an improved MobileSAM to perform instance segmentation. We enhanced MobileSAM's segmentation accuracy by fine-tuning the model, adapting MobileSAM from general instance segmentation tasks to the specific task of pig instance segmentation. Finally, we calculated the aspect ratio (LWR) of the minimum bounding rectangle of the mask from the pig's rear-view perspective. A decision tree model is employed to perform a non-linear mapping of the LWR value, classifying it into one of five predefined body condition score levels to determine the final grade. Our method effectively overcomes the limitations of traditional manual measurements. It features low computational cost, excellent realtime performance, and supports deployment on edge computing terminals in environments such as pig farms. Experimental results show that our method has significant advantages over other image classification and object detection methods in terms of accuracy and interpretability.

Dancers often prototype movements themselves or with each other during improvisation and choreography. How are these interactions altered when physically manipulable technologies are introduced into the creative process? To understand how dancers design and improvise movements while working with instruments capable of non-humanoid movements, we engaged dancers in workshops to co-create movements with a robot arm in one-human-to-one-robot and three-human-to-one-robot settings. We found that dancers produced more fluid movements in one-to-one scenarios, experiencing a stronger sense of connection and presence with the robot as a co-dancer. In three-to-one scenarios, the dancers divided their attention between the human dancers and the robot, resulting in increased perceived use of space and more stop-and-go movements, perceiving the robot as part of the stage background. This work highlights how technologies can drive creativity in movement artists adapting to new ways of working with physical instruments, contributing design insights supporting artistic collaborations with non-humanoid agents.

As drones become interwoven in human activities, increasingly taking on tasks interpreted as creative and performative, such as choreographed light shows, there is emerging interest in understanding how drones and humans can perform together. Humans have different habits when performing with partners as opposed to solo. How do people adapt their behaviors and perspectives when improvising with robotic partners? To explore these questions, we conducted a study investigating dancer-drone interactions using a system of micro aerial vehicles designed to facilitate improvised solo and partnered dances. Through solo and tandem dances with one or two robots, we analyzed the performers’ perceived workflow from semi-structured interviews and quantified their movement patterns during the improvisation. We found that the dancers perceived drone movements through spatial metaphors like the ceiling and gravity, anthropomorphizing drones as props on a stage through position and generated sound. The dancers felt a greater connection in single-drone scenarios and showed heightened avoidance behavior in two-drone situations. Our work shows how a robotic system can energize human dancers to improvise individually and in pairs.

Robots are increasingly deployed in crowded, large-scale environments where the demands on their services can outweigh their ability to respond. When robots fail to respond, humans may interpret the unintended consequence negatively as a form of rejection, leading to a loss of trust. How do service robots recover from such rejection to remediate human trust due to perceived rejection? We created a task mimicking shopping malls where the robot arm is asked to provide coffee, juice, or tea to participants. When the robot rendered service elsewhere, participants reported feeling excluded and less trusting of the robot. When the robot subsequently apologized or provided promise of future favor, participants regained trust in the robot, with favor rendering yielding significantly more trust responses. This study highlights the importance of understanding inadvertently negative consequences of robot behaviors, and suggests design solutions for overcoming this negative perception through remediation strategies.