Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
Authors: Daehee Lee, Minjong Yoo, Woo Kyung Kim, Wonje Choi, Honguk Woo
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Is Ci L and several adapter-based continual learning baselines across scenario variations based on complex, long-horizon tasks in the Franka-Kitchen and Meta-World environments to assess sample efficiency, task adaptation, and privacy considerations. |
| Researcher Affiliation | Academia | Sungkyunkwan University Carnegie Mellon University {dulgi7245, mjyoo2, kwk2696, wjchoi1995, hwoo}@skku.edu |
| Pseudocode | Yes | Algorithm 1 Is Ci L Skill Incremental Learning |
| Open Source Code | Yes | Yes, we provide the codes for supplementary material. |
| Open Datasets | Yes | To investigate the sample efficiency and adaptation performance, we construct complex Ci L scenarios using diverse long-horizon tasks [29, 30, 31]. |
| Dataset Splits | No | The paper mentions training and test data, but does not explicitly describe a validation dataset split or its specific use for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | Our experimental platform is powered by an AMD 5975wx CPU and 2x RTX 4090 GPUs. |
| Software Dependencies | Yes | We utilized jax 0.4.24, jaxlib 0.4.19, and flax 0.8.2 for our implementation. |
| Experiment Setup | Yes | Table 11: Pre-trained model configure and Table 12: Continual imitation learning default hyperparameters. |