Handling Complex Commands as Service Robot Task Requests
Authors: Vittorio Perera, Manuela Veloso
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 {vdperera,mmv}@cs.cmu.edu |
| 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. |