The wide availability of data coupled with the computational advances in artificial intelligence and machine learning promise to enable many future technologies such as autonomous driving. While there has been a variety of successful demonstrations of these technologies, critical system failures have repeatedly been reported. Even if rare, such system failures pose a serious barrier to adoption without a rigorous risk assessment. This article presents a framework for the systematic and rigorous risk verification of systems. We consider a wide range of system specifications formulated in signal temporal logic (STL) and model the system as a stochastic process, permitting discrete-time and continuous-time stochastic processes. We then define the STL robustness risk as the risk of lacking robustness against failure . This definition is motivated as system failures are often caused by missing robustness to modeling errors, system disturbances, and distribution shifts in the underlying data generating process. Within the definition, we permit general classes of risk measures and focus on tail risk measures such as the value-at-risk and the conditional value-at-risk. While the STL robustness risk is in general hard to compute, we propose the approximate STL robustness risk as a more tractable notion that upper bounds the STL robustness risk. We show how the approximate STL robustness risk can accurately be estimated from system trajectory data. For discrete-time stochastic processes, we show under which conditions the approximate STL robustness risk can even be computed exactly. We illustrate our verification algorithm in the autonomous driving simulator CARLA and show how a least risky controller can be selected among four neural network lane-keeping controllers for five meaningful system specifications.
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This content will become publicly available on May 13, 2025
Walking-by-Logic: Signal Temporal Logic-Guided Model Predictive Control for Bipedal Locomotion Resilient to External Perturbations
This study proposes a novel planning framework
based on a model predictive control formulation that incorporates
signal temporal logic (STL) specifications for task completion
guarantees and robustness quantification. This marks the
first-ever study to apply STL-guided trajectory optimization for
bipedal locomotion push recovery, where the robot experiences
unexpected disturbances. Existing recovery strategies often
struggle with complex task logic reasoning and locomotion robustness
evaluation, making them susceptible to failures due to
inappropriate recovery strategies or insufficient robustness. To
address this issue, the STL-guided framework generates optimal
and safe recovery trajectories that simultaneously satisfy the
task specification and maximize the locomotion robustness. Our
framework outperforms a state-of-the-art locomotion controller
in a high-fidelity dynamic simulation, especially in scenarios
involving crossed-leg maneuvers. Furthermore, it demonstrates
versatility in tasks such as locomotion on stepping stones, where
the robot must select from a set of disjointed footholds to
maneuver successfully.
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- NSF-PAR ID:
- 10535754
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-8457-4
- Page Range / eLocation ID:
- 1121 to 1127
- Format(s):
- Medium: X
- Location:
- Yokohama, Japan
- Sponsoring Org:
- National Science Foundation
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