Summary of Real-time Safe Control Of Neural Network Dynamic Models with Sound Approximation, by Hanjiang Hu et al.
Real-Time Safe Control of Neural Network Dynamic Models with Sound Approximation
by Hanjiang Hu, Jianglin Lan, Changliu Liu
First submitted to arxiv on: 20 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 Medium Difficulty Summary: This paper proposes a novel approach to safe control of Neural Network Dynamic Models (NNDMs) for robotics and other applications. The key challenge is computing an optimal safe control in real-time, which is addressed by using a sound approximation of the NNDM in control synthesis. Specifically, the authors introduce Bernstein over-approximated neural dynamics (BOND), which leverages the Bernstein polynomial over-approximation (BPO) of ReLU activation functions. To mitigate errors and ensure persistent feasibility, they synthesize a worst-case safety index using the most unsafe approximated state offline. For online real-time optimization, they formulate the first-order Taylor approximation as an additional linear layer with l2 bounded bias term for higher-order remainder. The authors demonstrate the effectiveness of their approach through comprehensive experiments on different neural dynamics and safety constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper is about making sure that robots can move safely without crashing or getting stuck. They use special computers to control these robots, but it’s hard to make them work fast enough while keeping everything safe. The authors come up with a new way to do this by simplifying the math and using something called “Bernstein over-approximated neural dynamics.” This makes their approach much faster than before, taking just a fraction of the time needed for older methods. They test it on different scenarios and show that it works really well. |
Keywords
» Artificial intelligence » Neural network » Optimization » Relu
