Summary of Data-driven Permissible Safe Control with Barrier Certificates, by Rayan Mazouz et al.
Data-Driven Permissible Safe Control with Barrier Certificates
by Rayan Mazouz, John Skovbekk, Frederik Baymler Mathiesen, Eric Frew, Luca Laurenti, Morteza Lahijanian
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Systems and Control (eess.SY)
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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 A novel method for identifying a maximal set of safe strategies is proposed for stochastic systems with unknown dynamics using barrier certificates. The approach involves learning the dynamics via Gaussian process (GP) regression, estimating probabilistic errors, and constructing piecewise stochastic barrier functions to find a maximal permissible strategy set. This algorithm ensures that the permissible strategies maintain probabilistic safety for the true system. The method is particularly useful for learning-enabled systems, which can benefit from a rich strategy space enabling additional data collection and complex behaviors while remaining safe. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make sure that robots or other machines behave safely even when they don’t know exactly how the world works. To do this, it uses special mathematical tools called barrier certificates to identify the safest ways for the machine to act. The approach involves learning about the system’s behavior using data and then using that information to find safe strategies. As more data is collected, the machine can adapt and behave in new and complex ways while still staying safe. |
Keywords
» Artificial intelligence » Regression
