Learning Qualitative Spatial Relations for Robotic Navigation
Authors: Abdeslam Boularias, Felix Duvallet, Jean Oh, Anthony Stentz
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed extensive experiments using the robotic platform shown in Figure 1. |
| Researcher Affiliation | Academia | Abdeslam Boularias Department of Computer Science Rutgers University Felix Duvallet Ecole Polytechnique F ed erale de Lausanne (EPFL) Jean Oh and Anthony Stentz Robotics Institute Carnegie Mellon University |
| Pseudocode | No | The paper contains mathematical equations but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links or statements regarding the open-sourcing of the code for the described methodology. |
| Open Datasets | No | The paper mentions using 'twenty examples for learning' and creating a 'world model with eleven objects' in simulation, but it does not provide access information (link, DOI, repository, or formal citation with author/year) for these datasets, nor does it refer to a well-known public dataset. |
| Dataset Splits | No | The paper mentions 'a validation example' in a figure caption but does not provide specific dataset split information (percentages, sample counts, or defined methodology) for training, validation, or test sets. |
| Hardware Specification | Yes | We performed extensive experiments using the robotic platform shown in Figure 1. (Figure 1 caption: Clearpath TM Husky robot used in the experiments) |
| Software Dependencies | No | The paper describes the methods used (e.g., Bayesian model, Imitation Learning, PMAP with Field D*) but does not specify any software names with version numbers for libraries or tools used in the implementation. |
| Experiment Setup | No | The paper mentions using 'gradient descent, with the l1 regularization' for learning weights but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, regularization strength) or other training configurations. |