Reactive and Risk-Aware Control for Signal Temporal Logic
Not on the map. Why?
Abstract (arXiv)
The deployment of autonomous systems in uncertain and dynamic environments has raised fundamental questions. Addressing these is pivotal to build fully autonomous systems and requires a systematic integration of planning and control. We first propose reactive risk signal interval temporal logic (ReRiSITL) as an extension of signal temporal logic (STL) to formulate complex spatiotemporal specifications. Unlike STL, ReRiSITL allows to consider uncontrollable propositions that may model humans as well as random environmental events such as sensor failures. Additionally, ReRiSITL allows to incorporate risk measures, such as (but not limited to) the Conditional Value-at-Risk, to measure the risk of violating certain spatial specifications. Second, we propose an algorithm to check if an ReRiSITL specification is satisfiable. For this purpose, we abstract the ReRiSITL specification into a timed signal transducer and devise a game-based approach. Third, we propose a reactive planning and control framework for dynamical control systems under ReRiSITL specifications.
Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). First posted on arXiv 30 Aug 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2021Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal controlcited by 12
- 2023Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learningcited by 3
- 2022Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectivescited by 1
- 2022Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Datacited by 0
- 2026Reward Machines for Signal Temporal Logiccited by 0
- 2026Reward Machines for Signal Temporal Logic13 Aug 2026
- 2023Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning31 Oct 2023
- 2022Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data12 Dec 2022
- 2022Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives14 Oct 2022
- 2021Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal control18 Sep 2021