Summary of Learning Control Barrier Functions and Their Application in Reinforcement Learning: a Survey, by Maeva Guerrier et al.
Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey
by Maeva Guerrier, Hassan Fouad, Giovanni Beltrame
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper explores the use of control barrier functions in safe reinforcement learning for developing new robot behaviors. It aims to address the issue of lack of safety guarantees in traditional reinforcement learning, enabling faster transfer to real robots and facilitating lifelong learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Robot learning is made safer with the help of control barrier functions that ensure the system stays within a safe state during the learning process. However, synthesizing these functions requires domain knowledge, making it challenging. The paper reviews existing literature on safe reinforcement learning using control barrier functions and explores data-driven methods for automatically defining them. |
Keywords
» Artificial intelligence » Reinforcement learning
