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 [1].
SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy Adaptation
Authors: Sangwoo Shin, Minjong Yoo, Jeongwoo Lee, Honguk Woo
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our framework with Meta-World, Franka Kitchen, RLBench, and CARLA environments. The results clarify the framework s superiority in performing long-horizon tasks and adapting to different domains, showing its broad applicability in practical use cases, such as cognitive robots interpreting abstract instructions and autonomous vehicles operating under varied configurations. |
| Researcher Affiliation | Academia | Sangwoo Shin, Minjong Yoo, Jeongwoo Lee, Honguk Woo* Department of Computer Science and Engineering, Sungkyunkwan University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Two-phase adaptation of Sem Tra |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | For cross-domain adaptation evaluations, we use the Franka Kitchen (Fu et al. 2020) (FK) and Meta World (Yu et al. 2019) (MW) environments. Detailed explanations for training datasets and examples of task prompts can be found in Appendix B. |
| Dataset Splits | No | The paper describes evaluation tasks and metrics, but does not provide specific train/validation/test dataset splits with percentages or sample counts in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU or CPU models, or memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions various models and frameworks (e.g., GPT2, Bloom, V-CLIP, VIMA) that were used or compared, but it does not list specific software dependencies with version numbers (e.g., PyTorch 1.x, Python 3.x) required to replicate the experimental setup. |
| Experiment Setup | No | The paper does not provide concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific training configurations for its models. |