Summary of Learning Performance-oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation, by Lakshmideepakreddy Manda et al.
Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation
by Lakshmideepakreddy Manda, Shaoru Chen, Mahyar Fazlyab
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes a novel self-supervised learning framework to address the challenges in designing Control Barrier Functions (CBFs) for nonlinear control systems. The authors introduce a smooth function that forms an inner approximation of the safe set, which is then combined with a neural network to parameterize the CBF candidate. A physics-informed loss function based on Hamilton-Jacobi PDEs is designed to train the CBF and maximize the volume of the resulting control invariant set. The efficacy of this approach is validated on two systems: a 2D double integrator and a 7D fixed-wing aircraft system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us design better control systems that stay safe. It’s like having a safety net that keeps our machines from getting out of control. The authors came up with a new way to make these “safety nets” using computer learning, which lets the system figure out how to keep itself safe without needing lots of human help. They tested this idea on two different systems and showed it works well. |
Keywords
* Artificial intelligence * Loss function * Neural network * Self supervised
