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 [1].
RoboCup@Home β Benchmarking Domestic Service Robots
Authors: Sven Wachsmuth, Dirk Holz, Maja Rudinac, Javier Ruiz-del-Solar
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Fig. 1 the performance of the best team in each of the tests deο¬ned by TC is analyzed over the last years with regard to the different skills (for more details see (Holz et al. 2014)). In order to illustrate how these statistics are used to drive certain rulebook changes, we will discuss two examples |
| Researcher Affiliation | Academia | Sven Wachsmuth Bielefeld University Germany EMAIL Dirk Holz University of Bonn Germany EMAIL Maja Rudinac Delft University of Technology The Netherlands EMAIL Javier Ruiz-del-Solar Universidad de Chile Chile EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses the RoboCup@Home competition and its development, but it does not present a specific methodology with accompanying source code for public access. |
| Open Datasets | No | The paper refers to statistics from the RoboCup@Home competition and mentions previous works in its references that might relate to datasets (e.g., Rawseeds), but it does not provide concrete access information (link, DOI, specific citation for access) for a publicly available dataset that was trained on as part of this paper's analysis. |
| Dataset Splits | No | The paper analyzes performance statistics from the RoboCup@Home competition but does not provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper discusses robot capabilities and competition tasks but does not provide specific hardware details (GPU/CPU models, memory amounts, or detailed computer specifications) used for any experiments conducted by the authors in this paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate any analysis or experiments. |
| Experiment Setup | No | The paper is a discussion of the RoboCup@Home league and its development, not a description of a specific experimental setup with hyperparameters or training configurations. |