Human-Agent-Robot Task Coordination in FiWi-Based Tactile Internet Infrastructures Using Context- and Self-Awareness
Why this work is in the frame
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Bibliographic record
Abstract
With the advent of safe collaborative robots, their seamless integration into human teams as teammates is starting to gain steam as part of the vision of the emerging Tactile Internet. The Tactile Internet lies at the nexus of computerization, automation, and robotization. While necessary, low task execution time and ultra-reliable human-robot connectivity are not sufficient to unleash the full potential of the resultant human-agent-robot teamwork (HART) applications. In this paper, we propose a context- and self-aware HART-centric allocation scheme for both physical and digital tasks to coordinate the automation and augmentation of mutually beneficial human-machine coactivities while spreading ownership of robots across users over integrated fiber-wireless (FiWi) Tactile Internet infrastructures. In addition to realizing collective context-awareness via HART-centric task coordination, we aim at exploiting local self-awareness in order to improve the energy-delay performance of robots. Further, we present an analytical framework to estimate the packet transmission delay and human-robot connection reliability. Our results indicate that our proposed context- and self-aware HART-centric task coordination scheme obtains a low task execution time while minimizing the energy consumption and operational expenditures (OPEX) of mobile robots.
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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) | 0.000 | 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