Bibliographic record
Abstract
Robots are rapidly advancing toward becoming autonomous and skilled entities in a wide range of environments, and it is likely that more and more people will soon be interacting with robots in their everyday lives. As this happens, we believe that it is crucial that robots are designed to be easy to use and understand, reducing the requirement for people and environments to adapt to the robot. Emerging research in Human-Robot Interaction (HRI) suggests that people have a strong natural tendency to treat robots as social entities, anthropomorphizing, zoomorphizing, and generally attributing them with social characteristics and roles. Our approach to HRI is to explicitly focus on the social layers of interaction, building robotic interfaces that use people’s existing skill sets, and that explicitly attempt to integrate into familiar social structures. We refer to this approach of directly considering HRI in the context of the social human world as social HRI. The field of social HRI is only just emerging: there is little general discussion which explains why social HRI is important or what exactly social HRI means, there is no methodology for approaching the specific consideration of designing and implementing social HRI interfaces, and there is no structured methodology for evaluating and studying social HRI. There are few social HRI interface designs and implementations—those that focus on social interaction between people and robots—and the scope of social HRI interaction possibilities is still relatively unexplored. In this dissertation we present what we believe is the first thorough exploration of the theory, design, implementation, and evaluation of social HRI. We present a detailed analysis of social HRI, drawing particularly from selected works in social psychology and philosophy, and compose a social HRI-targeted theory that addresses why people tend toward social interaction with robots, how robots can leverage this, and what the implications are for both users and designers. We present a set of social HRI interfaces we designed, implemented and evaluated as a means to demonstrate and reflect on the practical and technical feasibility of applying social HRI principles to robot interface designs. We present the results from several extensive user studies, which we conducted as a means to learn from our interfaces, and to test, reflect on and further develop our social HRI theories. Finally, we distill our overall efforts into a set of some of the very first social HRI-specific design heuristics. Overall, our work highlights the importance of considering the broad landscape of social interaction between people and robots, and the usefulness of explicitly considering social aspects for robotic interaction design. We hope that by establishing the foundational social HRI groundwork, this dissertation will lead to and support continuing development of social HRI theory and new social HRI interface designs as this emerging domain evolves and grows.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".