Verbalization: Narration of Autonomous Robot Experience
Authors: Stephanie Rosenthal, Sai P. Selvaraj, Manuela Veloso
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an algorithm for segmenting a path and mapping each segment to an utterance, as a function of the desired point in the verbalization space, and demonstrate its application using our mobile service robot moving in our buildings. |
| Researcher Affiliation | Academia | Stephanie Rosenthal Software Engineering Institute srosenthal@sei.cmu.edu Sai P. Selvaraj Robotics Institute spandise@andrew.cmu.edu Manuela Veloso Computer Science Department veloso@cs.cmu.edu Carnegie Mellon University, Pittsburgh USA |
| Pseudocode | Yes | Algorithm 1 Variable Verbalization Algorithm Input: route, verb pref, map Output: narrative |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper states 'Our robot s environment includes three buildings connected by bridges.' and 'The set of all floors and all buildings is defined as our map M.', referring to an internal map and corpus. No public access information (link, DOI, citation) is provided for this or any other dataset. |
| Dataset Splits | No | The paper states 'We randomly generated 12 multi-floor routes in our Gates building and 12 single-floor routes, ran the VV algorithm over the route plans, and analyzed the content of the 36 24 verbalizations that were generated.' This describes the data used for evaluation, but does not specify formal training, validation, or test dataset splits for model development and evaluation. |
| Hardware Specification | No | The paper mentions 'Our mobile service robot' and 'Our robot s environment' but provides no specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific names of software components, libraries, or programming languages with their version numbers that would be necessary to reproduce the experiment (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | No | The paper describes the parameters of its verbalization space (abstraction, locality, specificity) and the algorithm's steps, but it does not provide specific experimental setup details such as hyperparameters, learning rates, batch sizes, or optimizer settings typically found in machine learning experiments. |