Summary of Task-agnostic Pre-training and Task-guided Fine-tuning For Versatile Diffusion Planner, by Chenyou Fan et al.
Task-agnostic Pre-training and Task-guided Fine-tuning for Versatile Diffusion Planner
by Chenyou Fan, Chenjia Bai, Zhao Shan, Haoran He, Yang Zhang, Zhen Wang
First submitted to arxiv on: 30 Sep 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 The proposed two-stage framework, SODP, leverages sub-optimal data to learn a diffusion planner capable of generalizing to various downstream tasks. In the pre-training stage, a foundation diffusion planner is trained to extract planning capabilities by modeling the versatile distribution of multi-task trajectories. This model is then fine-tuned using RL-based methods with task-specific rewards for each downstream task, requiring only a small amount of data. The framework demonstrates superior performance on meta-world and adroit tasks, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SODP is a new way to teach machines how to plan and make decisions. Usually, we need a lot of human effort to collect information or design special rewards for the machine to learn. SODP uses existing data that might not be perfect, but it’s better than nothing. The framework has two stages: first, it trains a model to understand general planning abilities by looking at many examples of different tasks. Then, for each specific task, it fine-tunes the model using small amounts of special reward information. This approach shows promise in helping machines make better decisions. |
Keywords
» Artificial intelligence » Diffusion » Multi task
