Action Recognition for Human–Robot Teaming: Exploring Mutual Performance Monitoring Possibilities
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
Human–robot teaming (HrT) is being adopted in an increasing range of industries and work environments. Effective HrT relies on the success of complex and dynamic human–robot interaction. Although it may be optimal for robots to possess all the social and emotional skills to function as productive team members, certain cognitive capabilities can enable them to develop attitude-based competencies for optimizing teams. Despite the extensive research into the human–human team structure, the domain of HrT research remains relatively limited. In this sense, incorporating established human–human teaming (HhT) elements may prove practical. One key element is mutual performance monitoring (MPM), which involves the reciprocal observation and active anticipation of team members’ actions within the team setting, fostering enhanced team coordination and communication. By adopting this concept, this study uses ML-based visual action recognition as a potential tool for developing an effective way to monitor the human component in HrT. This study utilizes a data modeling approach on an existing dataset, the “Industrial Human Action Recognition Dataset” (InHARD), curated specifically for human action recognition assembly tasks in industrial environments involving human–robot collaborations. This paper presents the results of this modeling approach in analyzing the dataset to implement a theoretical concept that can be a first step toward enabling the system to adapt dynamically. The outcomes emphasize the significance of implementing state-of-the-art team concepts by integrating modern technologies and assessing the possibility of advancing HrT in this direction.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it