Summary of Obstacle-aware Gaussian Process Regression, by Gaurav Shrivastava
Obstacle-aware Gaussian Process Regression
by Gaurav Shrivastava
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR); Machine Learning (stat.ML)
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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 In this research paper, a new approach called “GP-ND” (Gaussian Process with Negative Datapairs) is proposed to enhance trajectory navigation in systems that need to avoid obstacles. The traditional Gaussian Process (GP) regression method fits curves to input-output pairs of data, but it lacks the ability to account for negative data points representing obstacle locations. To address this limitation, GP-ND constrains the GP model to avoid these negative datapoints by introducing small blobs of Gaussian distribution that maximize their KL divergence from the GP. The framework optimizes both positive and negative datapairs simultaneously, which outperforms traditional GP learning in experiments. This approach does not compromise scalability or convergence speed as data size increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For obstacle-avoiding navigation systems, researchers have developed a new way to use Gaussian Process regression called “GP-ND”. The traditional method fits curves to data points, but it doesn’t work well when there are obstacles that need to be avoided. To fix this, the GP-ND approach adds special “negative” datapoints that represent obstacle locations and helps the model stay away from them. This new way of using GPs works better than the old method in tests and is still fast and efficient even with lots of data. |
Keywords
» Artificial intelligence » Regression
