Safe Control under Uncertainty with Probabilistic Signal Temporal Logic
By Dorsa Sadigh, Ashish Kapoor. 2016
160 citations recorded by the source corpus.
Safe control of dynamical systems that satisfy temporal invariants expressing various safety properties is a challenging problem that has drawn the attention of many researchers. However, making the assumption that such temporal properties are deterministic is far from the reality. For example, a robotic system might employ a camera sensor and a machine learned system to identify obstacles. Consequently, the safety properties the controller has to satisfy, will be a function of the sensor data and the associated classifier. We propose a framework for achieving safe control. At the heart of our approach is the new Probabilistic Signal Temporal Logic (PrSTL), an expressive language to define stochastic properties, and enforce probabilistic guarantees on them. We also present an efficient algorithm to reason about safe controllers given the constraints derived from the PrSTL specification. One of the key distinguishing features of PrSTL is that the encoded logic is adaptive and changes as the system encounters additional data and updates its beliefs about the latent random variables that define the safety properties. We demonstrate our approach by deriving safe control of quadrotors and autonomous vehicles in dynamic environments.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Angela P. Schoellig
- Andreas Krause
- Felix Berkenkamp
- Matteo Turchetta
- Dorsa Sadigh
- Sanjit A. Seshia
- Carlos Guestrin
- Sergey Levine
In Computational Theory and Mathematics
- Felix Berkenkamp
- Matteo Turchetta
- Angela P. Schoellig
- Andreas Krause
- Sanjit A. Seshia
- Dorsa Sadigh
- Xiaojin Zhu
- Carlos Guestrin
Citations in the corpus
- Safe Model-based Reinforcement Learning with Stability Guarantees (2017, background): “For example, [23] models uncertain environmental constraints, while [24] uses approximate uncertainty propagation of GP dynamics along trajectories.”
- Towards Verified Artificial Intelligence (2016, background): “Since the guarantees many ML algorithms give are probabilistic, this will require the development of probabilistic logics and similar formalisms that can capture these guarantees (e.g., [48]).”