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Found 15 papers, 3 papers with code
BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat
Some existing environments provide high-fidelity simulations but are either not open source or are not adapted to the BVR air combat domain.
Deep Learning Based Situation Awareness for Multiple Missiles Evasion
As the effective range of air-to-air missiles increases, it becomes harder for human operators to maintain the situational awareness needed to keep a UAV safe.
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.
Behavior Trees in Robot Control Systems
In this paper we will give a control theoretic perspective on the research area of behavior trees in robotics.
Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning
The key idea of this letter is to improve performance of backward chained BTs by using the conditions identified in a theoretical convergence proof to setup the RL problems for individual controllers.
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.
A Survey of Behavior Trees in Robotics and AI
Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade.
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.
Underwater Caging and Capture for Autonomous Underwater Vehicles
After the initial cage is established, the system waits for a second sighting, and the possible opportunity to create a smaller, shrinkable cage.
RCAMP: A Resilient Communication-Aware Motion Planner for Mobile Robots with Autonomous Repair of Wireless Connectivity
Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information.
Behavior Trees in Robotics and AI: An Introduction
A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game.
Towards Blended Reactive Planning and Acting using Behavior Trees
In this paper, we show how a planning algorithm can be used to automatically create and update a Behavior Tree (BT), controlling a robot in a dynamic environment.
Learning of Behavior Trees for Autonomous Agents
Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems.
