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Found 6 papers, 0 papers with code
In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States
Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems.
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions
This feasibility analysis results in a set of richness conditions that the available information about the system should satisfy to guarantee that a safe control action can be found at all times.
Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty
However, since these constraints rely on a model of the system, when this model is inaccurate the guarantees of safety and stability can be easily lost.
Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics
This paper presents a method to design a min-norm Control Lyapunov Function (CLF)-based stabilizing controller for a control-affine system with uncertain dynamics using Gaussian Process (GP) regression.
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions
In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach.
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning
The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints.
