Summary of Real-time Adaptive Safety-critical Control with Gaussian Processes in High-order Uncertain Models, by Yu Zhang et al.
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
by Yu Zhang, Long Wen, Xiangtong Yao, Zhenshan Bing, Linghuan Kong, Wei He, Alois Knoll
First submitted to arxiv on: 29 Feb 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 The paper proposes an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. The approach consists of two phases: the first phase integrates a forgetting factor into a novel sparse Gaussian process (GP) algorithm, enhancing adaptability and computational efficiency. The second phase introduces a safety filter based on high-order control barrier functions (HOCBFs), leveraging the compound kernel from the first phase to address limitations in handling high-dimensional problems. The framework ensures a rigorous lower bound on the probability of satisfying the safety specification, as demonstrated through real-time obstacle avoidance experiments using both simulation and a 7-DOF robot. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates an adaptive online learning system that helps control systems with uncertain parameters stay safe in changing environments. It does this by combining two parts: first, it improves a type of algorithm called Gaussian Process (GP) by making it more adaptable and efficient. Then, it uses a safety filter based on high-order control barrier functions to ensure the system stays within safe limits. This approach is tested using simulations and a real-world robot. |
Keywords
* Artificial intelligence * Online learning * Probability
