Task Selection and Planning in Human-Robot Collaborative Processes: To be a Leader or a Follower?
Why this work is in the frame
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Bibliographic record
Abstract
Recent advances in collaborative robots have provided an opportunity for the close collaboration of humans and robots in a shared workspace. To exploit this collaboration, robots need to plan for optimal team performance while considering human presence and preference. This paper studies the problem of task selection and planning in a collaborative, simulated scenario. In contrast to existing approaches, which mainly involve assigning tasks to agents by a task allocation unit and informing them through a communication interface, we give the human and robot the agency to be the leader or follower. This allows them to select their own tasks or even assign tasks to each other. We propose a task selection and planning algorithm that enables the robot to consider the human’s preference to lead, as well as the team and the human’s performance, and adapts itself accordingly by taking or giving the lead. The effectiveness of this algorithm has been validated through a simulation study with different combinations of human accuracy levels and preferences for leading.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it