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Found 7 papers, 0 papers with code
Stable Autonomous Flow Matching
Despite this, connections between control theory and generative models in the literature are sparse, even though there are several machine learning applications with physically stable data points.
An Extended Convergence Result for Behaviour Tree Controllers
Behavior trees (BTs) are an optimally modular framework to assemble hierarchical hybrid control policies from a set of low-level control policies using a tree structure.
Continuous-Time Behavior Trees as Discontinuous Dynamical Systems
In this letter, we provide the first continuous-time formulation of behavior trees, show that they can be seen as discontinuous dynamical systems (a subclass of hybrid dynamical systems), which enables the application of existence and uniqueness results to behavior trees, and finally, provide sufficient conditions under which such systems will converge to a desired region of the state space for general designs.
PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM
However, an accurate estimate of the uncertainty of such registration is a key requirement to a consistent fusion of this kind of measurements in a SLAM filter.
Learning Dynamic-Objective Policies from a Class of Optimal Trajectories
In this paper, we present a novel and straightforward approach to synthesising these policies through a combination of trajectory optimisation, homotopy continuation, and imitation learning.
Improving the Modularity of AUV Control Systems using Behaviour Trees
In this paper, we show how behaviour trees (BTs) can be used to design modular, versatile, and robust control architectures for mission-critical systems.
Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees
In this paper, we show how Behavior Trees that have performance guarantees, in terms of safety and goal convergence, can be extended with components that were designed using machine learning, without destroying those performance guarantees.
