Playing with Telepresence Robots for Design Speculation
Bibliographic record
Abstract
This workshop explores how telepresence robots can be used for playful design speculation, leveraging their inherent asymmetries to create engaging and innovative experiences.By focusing on playfulness instead of purely utilitarian applications this workshop seeks to transform the limitations of telepresence robots into opportunities for creative interaction.We wish to explore the ways in which we can exploit the asymmetrical capabilities of remote and local users of telepresence robots.Given the person using the robot will always have more constraints due to the technical limitations of the robot (e.g., limited movement, limited space awareness, etc), we want to investigate if moving away from utilitarian applications towards playfulness can help make these robots more attractive and useful.In this workshop participants will adapt physical games using embodied methods embodied design ideation methods, such as magic machines, embodied sketching, and soma bits, to create playful interventions with robots and to discover new ways to enhance telepresence technology.The focus is on embracing asymmetry to foster innovative, inclusive, and enjoyable interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".