Summary of Stochastic Online Optimization For Cyber-physical and Robotic Systems, by Hao Ma et al.
Stochastic Online Optimization for Cyber-Physical and Robotic Systems
by Hao Ma, Melanie Zeilinger, Michael Muehlebach
First submitted to arxiv on: 8 Apr 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 The proposed gradient-based online optimization framework addresses stochastic programming problems that arise in cyber-physical and robotic systems. The framework incorporates constraints modeling the evolution of a cyber-physical system with continuous state and action spaces, nonlinearity, and partial observation. The approach also incorporates approximate dynamics as prior knowledge, demonstrating significant improvement in convergence even with rough estimates. The online optimization framework encompasses both gradient descent and quasi-Newton methods, with a unified convergence analysis in non-convex settings. Additionally, the impact of modeling errors on convergence rate is characterized. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to optimize systems that are affected by uncertainty, like robots or machines. It’s trying to solve a problem that happens when you have a system that can’t be fully observed and needs to make decisions based on incomplete information. The approach uses gradient-based methods and incorporates knowledge about the system’s dynamics. This can help improve the optimization process even with rough estimates of the system’s behavior. |
Keywords
» Artificial intelligence » Gradient descent » Optimization
