Bibliographic record
Abstract
Human responses towards robots vary from excitement and acceptance to complete rejection, and as robots become more prevalent in personal and public spaces, understanding the factors that elicit those responses is important. This research explores the factors influencing these responses, specifically in the context of human-robot collaborative tasks. Under mechatronics and robotics, the study focuses on identifying behavioural features (like triggers or positive/negative eliciting actions) and human-designed robot algorithms that elicit different human reactions. It also examines how these features interact to shape human behaviour and emotions in stressful and calm situations. I developed a study in a simulated medical setting where participants are tasked with developing dialogue scripts for a social robot. The study utilizes the NAO robot and a programmable platform called the Robot Management System developed by RobotLab. Participants will program NAO to interact with a patient, perform a checkup, and decide on actions like writing a prescription or referring the patient to medical professionals. The experiment is designed to observe how the participant interacts with the robot to complete tasks and how the robot’s design impacts the participants' perception and interaction following and during tasks. While no experimental data has been collected due to pending ethics approval, the research will provide insights into human-robot interaction, including algorithm design. This study aims to enhance our understanding of human-robot relationships, contributing to developing robotic systems that can be effectively integrated into social environments, such as healthcare settings or similar environments. These insights may inform the design and deployment of robots to ensure they support rather than disrupt social dynamics.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".