Apprenticeship Scheduling for Human-Robot Teams
Authors: Matthew Gombolay
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | I have been conducting a series of human-subject experiments in which a team of two human team members, the subject and one confederate, and one robot team member work together to complete a set of tasks in an experimental setup analogous to a manufacturing environment. ... We found that increasing the robot s authority over task allocation decisions decreased the time to schedule the team and the time to execute and improved the humans perception of their robotic counterpart. ... I have conducted a promising validation on a synthetic data set of solutions for a variety of scheduling problems and a real-world data set of demonstrations from human experts solving a resource optimization problem (Gombolay 2015). |
| Researcher Affiliation | Academia | Matthew C. Gombolay Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge, Massachusetts 02139 gombolay@csail.mit.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions a YouTube link for a demonstration of their algorithm, Tercio, but does not provide concrete access to the source code for the methodology described. |
| Open Datasets | No | The paper states, 'I have conducted a promising validation on a synthetic data set of solutions for a variety of scheduling problems and a real-world data set of demonstrations from human experts solving a resource optimization problem (Gombolay 2015).' However, it does not provide concrete access information (specific link, DOI, repository, or explicit statement of public availability) for these datasets. |
| Dataset Splits | No | The paper mentions using synthetic and real-world datasets but does not provide specific dataset split information (e.g., exact percentages, sample counts, or detailed splitting methodology) for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes a human-subject experiment and a 'scalable computational models and techniques' but does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |