Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning
Authors: Xing Lei, Xuetao Zhang, Donglin Wang
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 15,619 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,164 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that MGDA significantly improves the performance of GCWSL methods on both state-based and visionbased maze datasets, outperforming previous goal data augmentation techniques in their ability to enhancing stitching capabilities. |
| Researcher Affiliation | Academia | 1National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University 2School of Enginneering, Westlake University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: MGDA for GCWSL Methods |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | Yes | Datasets. To rigorously evaluate the stitching capabilities of GCWSL methods, we employ the offline point maze dataset configuration as outlined in (Ghugare et al. 2024). |
| Dataset Splits | No | The paper mentions evaluating on |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions that |
| Experiment Setup | No | The paper states: |