Discrete Latent Plans via Semantic Skill Abstractions
Authors: Haobin Jiang, Wang, Zongqing Lu
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | 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. |