Summary of Gsl-pcd: Improving Generalist-specialist Learning with Point Cloud Feature-based Task Partitioning, by Xiu Yuan
GSL-PCD: Improving Generalist-Specialist Learning with Point Cloud Feature-based Task Partitioning
by Xiu Yuan
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 paper proposes Generalist-Specialist Learning (GSL) with Point Cloud Feature-based Task Partitioning (GSL-PCD) to improve efficiency in Deep Reinforcement Learning (DRL) across unseen environment variations. The GSL framework trains a generalist model on all scenarios and then creates specialists from its weights, each focusing on a subset of variations. However, random task partitioning can impede performance by assigning vastly different variations to the same specialist, which raises computational costs. To address this, GSL-PCD clusters environment variations based on features extracted from object point clouds and uses balanced clustering with a greedy algorithm to assign similar variations to the same specialist. The proposed approach outperforms vanilla partitioning in robotic manipulation tasks from the ManiSkill benchmark by 9.4% with fewer specialists, reducing computational and sample requirements by 50%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps deep learning computers learn better by organizing information about different scenarios into generalist and specialist models. It creates a framework called Generalist-Specialist Learning (GSL) that trains on many scenarios and then breaks them down into smaller groups for each model to focus on. The problem is that current methods don’t organize these scenarios well, so the computer spends too much time and effort learning about each one. To fix this, the paper proposes using special features from objects’ 3D shapes (point clouds) to group similar scenarios together. This helps the computer learn faster and better. |
Keywords
* Artificial intelligence * Clustering * Deep learning * Reinforcement learning
