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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Authors: Ruipeng Zhang, Chenning Yu, Jingkai Chen, Chuchu Fan, Sicun Gao
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the proposed methods can significantly accelerate online planning over state-of-the-art complete dynamic planning algorithms. We evaluate the proposed approach in various challenging dynamic motion planning environments ranging from 2-Do F to 7-Do F KUKA arms. |
| Researcher Affiliation | Academia | The provided text does not contain explicit institutional affiliations (university/company names or email domains) for the authors. Therefore, classification of affiliation type is not possible from the given information. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks with explicit labels like 'Algorithm' or 'Pseudocode'. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement about code release. |
| Open Datasets | No | The paper states 'We randomly generate 2000 problems for training and 1000 problems for testing.' indicating a custom-generated dataset, but it does not provide concrete access information (e.g., link, DOI, or formal citation to a public dataset). |
| Dataset Splits | No | The paper specifies generated training and testing data ('We randomly generate 2000 problems for training and 1000 problems for testing.') but does not explicitly mention a validation dataset split or its size/percentage. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers required to replicate the experiment. |
| Experiment Setup | Yes | We first train the GNN-TE on all the training problems for 200 epochs. Afterward, we generate 1000 new training data with DAgger, and trained for another 100 epochs. |