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..
Discrete Latent Plans via Semantic Skill Abstractions
Authors: Haobin Jiang, Wang, Zongqing Lu
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments in simulated control environments, we demonstrate that LADS outperforms state-of-the-art methods in both skill learning and compositional generalization. The code is available at https://github.com/PKU-RL/LADS. 5 EXPERIMENTS |
| Researcher Affiliation | Academia | Haobin Jiang1, Jiangxing Wang1, Zongqing Lu1,2 1School of Computer Science, Peking University 2Beijing Academy of Artificial Intelligence Correspondence to Zongqing Lu <EMAIL>. |
| Pseudocode | Yes | We provide the pseudocode of LADS as shown in Algorithm 1. Algorithm 1 Training LADS |
| Open Source Code | Yes | The code is available at https://github.com/PKU-RL/LADS. |
| Open Datasets | Yes | LORe L (Nair et al., 2022) is a simulated domain developed on top of Meta-World (Yu et al., 2020)... Kitchen (Gupta et al., 2019) is a simulated domain developed on top of Mu Jo Co (Todorov et al., 2012)... |
| Dataset Splits | Yes | For testing, we selected 3 instructions, resulting in a training dataset of 22 instructions and 509 demonstrations. |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU types, or memory amounts) used for running experiments are provided in the paper. |
| Software Dependencies | No | We use a pretrained Distil BERT (Sanh, 2019) as the language encoder and a causal transformer (Chen et al., 2021a) as the high-level policy. ... We use CLIP Vi T-B/32 to encode the instruction... For Kitchen (image), we use a pretrained Res Net18 (He et al., 2016) to encode the images... |
| Experiment Setup | Yes | The hyperparameters for the network architecture not covered in Appendix B.1, as well as those related to training, are listed in Table 4. Table 4: Hyperparameters of our experiments. |