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Found 8 papers, 4 papers with code
Bounding Stochastic Safety: Leveraging Freedman's Inequality with Discrete-Time Control Barrier Functions
In contrast, this paper utilizes Freedman's inequality in the context of discrete-time control barrier functions (DTCBFs) and c-martingales to provide stronger (less conservative) safety guarantees for stochastic systems.
Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions
A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances.
Input-to-State Stability in Probability
Input-to-State Stability (ISS) is fundamental in mathematically quantifying how stability degrades in the presence of bounded disturbances.
Robust Safety under Stochastic Uncertainty with Discrete-Time Control Barrier Functions
To this end, we leverage Control Barrier Functions (CBFs) which guarantee that a robot remains in a ``safe set'' during its operation -- yet CBFs (and their associated guarantees) are traditionally studied in the context of continuous-time, deterministic systems with bounded uncertainties.
End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions
Imitation learning (IL) is a learning paradigm which can be used to synthesize controllers for complex systems that mimic behavior demonstrated by an expert (user or control algorithm).
Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
Existing design paradigms do not address the gap between theory (controller design with continuous time models) and practice (the discrete time sampled implementation of the resulting controllers); this can lead to poor performance and violations of safety for hardware instantiations.
Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State
The increasing complexity of modern robotic systems and the environments they operate in necessitates the formal consideration of safety in the presence of imperfect measurements.
Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions
The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions.
