Integrating Answer Set Programming with Semantic Dictionaries for Robot Task Planning
Authors: Dongcai Lu, Yi Zhou, Feng Wu, Zhao Zhang, Xiaoping Chen
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we evaluated our approach using common benchmarks on service tasks and showed that it can successfully handle much more tasks than the state-of-the-art solution. Notably, we deployed the proposed planning system on our service robot for the annual Robo Cup@Home competitions and achieved very encouraging results. |
| Researcher Affiliation | Academia | Dongcai Lu1 Yi Zhou2 Feng Wu1 Zhao Zhang1 Xiaoping Chen1 1School of Computer Science and Technology, University of Science and Technology of China, China 2School of Computing, Engineering and Mathematics, University of Western Sydney, Australia |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes the conversion to ASP rules in prose and formulas. |
| Open Source Code | No | The paper does not provide a direct link to the source code for the methodology. It mentions the robot system and a demo video, but not the code itself. |
| Open Datasets | Yes | In the experiments, we evaluated our ASP planner on the common benchmarks widely used for domestic service robots [Chen et al., 2013; 2012; Kunze et al., 2010; Tenorth and Beetz, 2013], consisting of two data sets with 11885 task-oriented user instructions from the Tasks/Steps table and 467 desire-oriented user instructions from the Help table of OMICS. |
| Dataset Splits | No | The paper describes two datasets (OMICS Tasks/Steps and Help table) and mentions different configurations ('local' and 'global' search, and with/without Word Net). However, it does not specify explicit training, validation, or test dataset splits (e.g., percentages, counts, or specific predefined splits with citations). |
| Hardware Specification | No | The paper mentions deploying the system on 'our service robot' but does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for the experiments. |
| Software Dependencies | No | The paper mentions using 'semantic parser SEMAFOR proposed by [Das et al., 2012]', 'ASP solvers iclingo/oclingo', and 'tools, including PREPOST [Sil et al., 2010] and some open information extraction tools [Angeli et al., 2015]'. However, it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | No | The paper describes the system architecture and components but does not provide specific details about experimental setup such as hyperparameters, learning rates, batch sizes, or optimizer settings. It mentions 'two sets of configurations' regarding search method and Word Net usage, but these are not typical experimental setup parameters like hyperparameters. |