Editorial: The Art of Human-Robot Interaction: Creative Perspectives From Design and the Arts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Art of Human-Robot Interaction: Creative Perspectives from Design and the Arts Advancements in robotics have traditionally been considered the domain of engineering and computer science. However, cross-disciplinary collaborations between the arts and engineering can help drive innovation and technical solutions in robotics and fuel innovation in contemporary art (Stelarc, 2016, Goldberg, 2001. As robotic technologies mature and move beyond research laboratories and the factory floor, there is a greater emphasis and need to understand how to design and implement interactive and collaborative robots in the real world alongside people.User-centred and participatory design methods are well-established in the HCI domain (Wilkinson and De Angeli, 2014), and there is a push to establish similar processes for robot design. Art and design help stimulate this process by involving end-users in the design process and cultivating interdisciplinary approaches to exploring the frontiers of HRI in the real world. One method for bridging artistic and engineering practices is through workshops that explore diverse disciplinary perspectives to find common ground and identify relevant design principles. Since the early 2010s, many international workshops, forums and programs have explored cross-disciplinary research in robots and art 1 (Smart et al., 2010, St-Onge, 2019. This research topic expands on ideas and discoveries made by this emerging community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it