Non-Classical Planning for Robotic Applications
Authors: Scott Kiesel
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our implementation of Hindsight Optimization, we would generate possible worlds... The action would then be executed, new sensor data would arrive, new world samples would be generated, planning would happen again and finally a new next action would be selected. |
| Researcher Affiliation | Academia | Scott Kiesel University of New Hampshire skiesel@cs.unh.edu |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks found in the paper. |
| Open Source Code | No | The paper does not provide an unambiguous statement or link for open-source code specific to the methodology described in this paper. |
| Open Datasets | No | The paper mentions specific domains like "Search and Rescue domain" and "pick and place tasks around the house," but it does not provide concrete access information (link, DOI, citation with authors/year) for these as publicly available datasets. |
| Dataset Splits | No | The paper does not specify exact training, validation, or test dataset splits. It describes problem domains but not data partitioning details. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, cloud instances) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings. |