Summary of Resisting Stochastic Risks in Diffusion Planners with the Trajectory Aggregation Tree, by Lang Feng et al.
Resisting Stochastic Risks in Diffusion Planners with the Trajectory Aggregation Tree
by Lang Feng, Pengjie Gu, Bo An, Gang Pan
First submitted to arxiv on: 28 May 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 novel Trajectory Aggregation Tree (TAT) approach addresses the reliability and stability issues of diffusion planners by aggregating information from historical and current trajectories. This dynamic tree-like structure prioritizes impactful nodes for decision-making, without modifying original training or sampling pipelines. TAT ensures 100% task performance boosting, with an appreciable tolerance margin for sample quality, allowing for more than 3x acceleration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TAT helps diffusion planners handle long-horizon and sparse-reward tasks better. It uses past and current trajectories to make good decisions. This method works without changing how the planner is trained or samples new data. TAT makes the planner perform well in all tasks, even with low-quality data, which can speed up planning by more than three times. |
Keywords
» Artificial intelligence » Boosting » Diffusion
