Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Handling Complex Commands as Service Robot Task Requests
Authors: Vittorio Perera, Manuela Veloso
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show results on a corpus of 100 complex commands. In order to evaluate our template-based algorithm, we gathered a corpus of 100 complex commands. The approach proposed is able to correctly break 72% of the commands in their atomic components. Finally, we evaluate the robot execution planning algorithm on a set of 150 randomly generated complex commands. We compare our approach to a base line and show that we can consistently improve on it. |
| Researcher Affiliation | Academia | Vittorio Perera and Manuela Veloso Carnegie Mellon University Pittsburgh, PA EMAIL |
| Pseudocode | Yes | Algorithm 1, Algorithm 2, Algorithm 3 |
| Open Source Code | No | The paper provides a link for the corpus: 'This corpus is available at http://www.cs.cmu.edu/ vdperera/corpora/', but there is no explicit statement or link providing access to the source code for the methodology. |
| Open Datasets | Yes | We gathered a corpus of 100 complex commands by asking 10 users to give each 10 commands. (This corpus is available at http://www.cs.cmu.edu/ vdperera/corpora/) |
| Dataset Splits | No | The paper describes using a corpus of 100 complex commands and a set of 150 randomly generated commands for evaluation, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, memory, or processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the algorithms and their evaluations but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |