Human-Agent-Robot Teamwork (HART) Over FiWi-Based Tactile Internet Infrastructures
Bibliographic record
Abstract
To facilitate making the human-machine co-activity-based human-agent-robot teamwork (HART) task execution process more efficient, this chapter first discusses related work and open challenges for latency-sensitive HART task. To speed up the HART task execution, this chapter next presents a latency sensitive HART task migration scheme for efficiently orchestrating tasks among human mobile users (MUs), central and decentralized computational agents (cloud/cloudlets), and robots across converged FiWi network infrastructures. Moreover, this chapter describes a bandwidth allocation scheme that allocates timeslots to MUs' broadband and task migration traffic at the same time. Furthermore, this chapter presents performance evaluation results of the proposed scheme. Importantly, this chapter compares the performance of the proposed task migration scheme with traditional schemes. This chapter is finally concluded by summarizing important findings and outlining open research issues for HART task coordination over FiWi-enhanced tactile internet infrastructures.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".