Summary of Incremental Learning Of Retrievable Skills For Efficient Continual Task Adaptation, by Daehee Lee et al.
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
by Daehee Lee, Minjong Yoo, Woo Kyung Kim, Wonje Choi, Honguk Woo
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces IsCiL, an adapter-based framework that enables Continual Imitation Learning (CiL) to achieve multi-task policies by extracting and accumulating task knowledge from demonstrations across multiple stages and tasks. The approach addresses the limitation of isolating parameters for specific tasks, mitigating catastrophic forgetting while enabling sample-efficient task adaptation in non-stationary CiL environments. IsCiL incrementally learns shareable skills from different demonstrations through prototype-based memory, mapping demonstrations into state embedding spaces to retrieve proper skills upon input states. Experimental results on complex tasks in Franka-Kitchen and Meta-World demonstrate robust performance of IsCiL for both task adaptation and sample-efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary IsCiL is a new way for machines to learn from doing things over and over again. This helps them get better at multiple tasks by sharing knowledge between different activities. The usual approach was limited, as it only kept information specific to each task, forgetting what it learned before. IsCiL changes this by allowing machines to remember useful skills they’ve learned across many demonstrations. This makes the machine more efficient and able to adapt quickly to new tasks. |
Keywords
* Artificial intelligence * Multi task
