A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems
| Autor: | Jaime F. Fisac, Anayo K. Akametalu, Melanie N. Zeilinger, Jeremy H. Gillula, Shahab Kaynama, Claire J. Tomlin |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Mathematical optimization Computer science Systems and Control (eess.SY) 02 engineering and technology Robot learning Vehicle dynamics Computer Science - Robotics 020901 industrial engineering & automation Reachability Control theory FOS: Electrical engineering electronic engineering information engineering Reinforcement learning Electrical and Electronic Engineering I.2.6 I.2.8 I.2.9 Probabilistic logic Constraint satisfaction Computer Science Applications Control and Systems Engineering Computer Science - Systems and Control Robot Robotics (cs.RO) |
| Zdroj: | IEEE Transactions on Automatic Control. 64:2737-2752 |
| ISSN: | 2334-3303 0018-9286 |
| Popis: | The proven efficacy of learning-based control schemes strongly motivates their application to robotic systems operating in the physical world. However, guaranteeing correct operation during the learning process is currently an unresolved issue, which is of vital importance in safety-critical systems. We propose a general safety framework based on Hamilton-Jacobi reachability methods that can work in conjunction with an arbitrary learning algorithm. The method exploits approximate knowledge of the system dynamics to guarantee constraint satisfaction while minimally interfering with the learning process. We further introduce a Bayesian mechanism that refines the safety analysis as the system acquires new evidence, reducing initial conservativeness when appropriate while strengthening guarantees through real-time validation. The result is a least-restrictive, safety-preserving control law that intervenes only when (a) the computed safety guarantees require it, or (b) confidence in the computed guarantees decays in light of new observations. We prove theoretical safety guarantees combining probabilistic and worst-case analysis and demonstrate the proposed framework experimentally on a quadrotor vehicle. Even though safety analysis is based on a simple point-mass model, the quadrotor successfully arrives at a suitable controller by policy-gradient reinforcement learning without ever crashing, and safely retracts away from a strong external disturbance introduced during flight. Comment: Accepted for publication in IEEE Transactions on Automatic Control. Video with experiments: https://youtu.be/WAAxyeSk2bw |
| Databáze: | OpenAIRE |
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