arXiv subjects

Petter Ögren

Publications and source records attributed to Petter Ögren.

At least 19 recordsLinked to original sources

arXiv2026

Agent Utilities over Generalized Voronoi Regions and their Gradients

In this paper, we generalize the concept of Voronoi regions, define agent utility as the integral of a utility density over the corresponding Voronoi region, derive gradients of the utility, and illustrate the approach in a two-team example from soccer. The generalization of Voronoi regions is in the form of so-called Cost-Induced Voronoi (CIV) regions, where the agent state space may differ from the space being partitioned. One example of such regions is when the cost is given by the optimal solution of an LQR control problem. Then the agent states include position as well as velocity, while the partitioned space only includes positions. The agent utility is defined by integrating some utility density over the CIV region of the agent. This utility density might be the probability density of some beneficial event, such as receiving a pass in soccer. The utility is then the overall probability of receiving a pass and the gradient represents a way to improve that probability. We show how this utility gradient can be computed using the Reynolds Transport Theorem from fluid mechanics, and that this approach achieves similar accuracy while reducing computation time by about an order of magnitude compared to a baseline finite-difference approximation.

cs.RO↗

arXiv2026

Learning Ordinal Response Policies in Rank-Based Stochastic Prize-Collecting Games

The Team Orienteering Problem (TOP) generalizes many real-world multi-agent scheduling and routing tasks that occur in autonomous mobility, aerial logistics, and surveillance applications. While many flavors of the TOP exist for planning in multi-agent systems, they assume that all the agents cooperate toward a single objective; therefore, they do not extend to settings when they compete in reward-scarce environments. We propose Stochastic Prize-Collecting Orienteering Games (SPCOG) as an extension of the TOP to plan in the presence of self-interested agents operating on a graph, under energy constraints and stochastic transitions. A theoretical discussion on complete and star graphs establishes that there is a unique pure Nash equilibrium in SPCOGs that coincides with the optimal routing solution of an equivalent TOP under rank-based conflict resolution. We propose the concept of Ordinal Rank (OR) as a concise representation of an agents' global rank and its location within a topological, well-defined neighborhood. Empirical evaluations conducted on real-world, road-network graphs under both dynamic and stationary prize distributions show that in parameter-sharing settings, the policies that leverage local information can outperform those policies leverage global information when the former is conditioned on the OR rather than the global rank, indicating that the OR acts as a strong inductive bias in multi-agent games on graphs. The OR-conditioned policies also generalize much better to games with large number of agents compared to global-rank conditioned policies. Finally, we also propose we propose Fictitious Ordinal Response Learning (FORL) as an entropy-regulated algorithm to obtain convergent policies in independent-learning settings in prize-collecting games on graphs.

cs.RO↗

arXiv2026

Least Restrictive Hyperplane Control Barrier Functions

Control Barrier Functions (CBFs) can provide provable safety guarantees for dynamic systems. However, finding a valid CBF for a system of interest is often non-trivial, especially for systems having low computational resources, higher-order dynamics, and moving close to obstacles of complex shape. A common solution to this problem is to use a purely distance-based CBF. In this paper, we study Hyperplane CBFs (H-CBFs), where a hyperplane separates the agent from the obstacle. First, we note that the common distance-based CBF is a special case of an H-CBF where the hyperplane is a supporting hyperplane of the obstacle that is orthogonal to a line between the agent and the obstacle. Then we show that a less conservative CBF can be found by optimising over the orientation of the supporting hyperplane, in order to find the Least Restrictive Hyperplane CBF. This enables us to maintain the safety guarantees while allowing controls that are closer to the desired ones, especially when moving fast and passing close to obstacles. We illustrate the approach on a double integrator dynamical system with acceleration constraints, moving through a group of arbitrarily shaped static and moving obstacles.

cs.RO↗

arXiv2026

Progress Constraints for Reinforcement Learning in Behavior Trees

Behavior Trees (BTs) provide a structured and reactive framework for decision-making, commonly used to switch between sub-controllers based on environmental conditions. Reinforcement Learning (RL), on the other hand, can learn near-optimal controllers but sometimes struggles with sparse rewards, safe exploration, and long-horizon credit assignment. Combining BTs with RL has the potential for mutual benefit: a BT design encodes structured domain knowledge that can simplify RL training, while RL enables automatic learning of the controllers within BTs. However, naive integration of BTs and RL can lead to some controllers counteracting other controllers, possibly undoing previously achieved subgoals, thereby degrading the overall performance. To address this, we propose progress constraints, a novel mechanism where feasibility estimators constrain the allowed action set based on theoretical BT convergence results. Empirical evaluations in a 2D proof-of-concept and a high-fidelity warehouse environment demonstrate improved performance, sample efficiency, and constraint satisfaction, compared to prior methods of BT-RL integration.

cs.AI↗

arXiv2025

SMaRCSim: Maritime Robotics Simulation Modules

Developing new functionality for underwater robots and testing them in the real world is time-consuming and resource-intensive. Simulation environments allow for rapid testing before field deployment. However, existing tools lack certain functionality for use cases in our project: i) developing learning-based methods for underwater vehicles; ii) creating teams of autonomous underwater, surface, and aerial vehicles; iii) integrating the simulation with mission planning for field experiments. A holistic solution to these problems presents great potential for bringing novel functionality into the underwater domain. In this paper we present SMaRCSim, a set of simulation packages that we have developed to help us address these issues.

cs.RO↗

arXiv2025

Multi-Agent Obstacle Avoidance using Velocity Obstacles and Control Barrier Functions

Velocity Obstacles (VO) methods form a paradigm for collision avoidance strategies among moving obstacles and agents. While VO methods perform well in simple multi-agent environments, they don't guarantee safety and can show overly conservative behavior in common situations. In this paper, we propose to combine a VO-strategy for guidance with a CBF-approach for safety, which overcomes the overly conservative behavior of VOs and formally guarantees safety. We validate our method in a baseline comparison study, using 2nd order integrator and car-like dynamics. Results support that our method outperforms the baselines w.r.t. path smoothness, collision avoidance, and success rates.

cs.RO↗

arXiv2024

BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat

Creating new air combat tactics and discovering novel maneuvers can require numerous hours of expert pilots' time. Additionally, for each different combat scenario, the same strategies may not work since small changes in equipment performance may drastically change the air combat outcome. For this reason, we created a reinforcement learning environment to help investigate potential air combat tactics in the field of beyond-visual-range (BVR) air combat: the BVR Gym. This type of air combat is important since long-range missiles are often the first weapon to be used in aerial combat. Some existing environments provide high-fidelity simulations but are either not open source or are not adapted to the BVR air combat domain. Other environments are open source but use less accurate simulation models. Our work provides a high-fidelity environment based on the open-source flight dynamics simulator JSBSim and is adapted to the BVR air combat domain. This article describes the building blocks of the environment and some use cases.

cs.LG↗

arXiv2024

Improving the performance of Learned Controllers in Behavior Trees using Value Function Estimates at Switching Boundaries

Behavior trees represent a modular way to create an overall controller from a set of sub-controllers solving different sub-problems. These sub-controllers can be created in different ways, such as classical model based control or reinforcement learning (RL). If each sub-controller satisfies the preconditions of the next sub-controller, the overall controller will achieve the overall goal. However, even if all sub-controllers are locally optimal in achieving the preconditions of the next, with respect to some performance metric such as completion time, the overall controller might be far from optimal with respect to the same performance metric. In this paper we show how the performance of the overall controller can be improved if we use approximations of value functions to inform the design of a sub-controller of the needs of the next one. We also show how, under certain assumptions, this leads to a globally optimal controller when the process is executed on all sub-controllers. Finally, this result also holds when some of the sub-controllers are already given, i.e., if we are constrained to use some existing sub-controllers the overall controller will be globally optimal given this constraint.

cs.RO↗

arXiv2024

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. In this work, we propose a decision support tool to help UAV operators in Beyond Visual Range (BVR) air combat scenarios assess the risks of different options and make decisions based on those. Earlier work focused on the threat posed by a single missile, and in this work, we extend the ideas to several missile threats. The proposed method uses Deep Neural Networks (DNN) to learn from high-fidelity simulations to provide the operator with an outcome estimate for a set of different strategies. Our results demonstrate that the proposed system can manage multiple incoming missiles, evaluate a family of options, and recommend the least risky course of action.

cs.LG↗

arXiv2023

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. Many robotic tasks are naturally decomposed into a hierarchy of control tasks, and modularity is a well-known tool for handling complexity, therefor behavior trees have garnered widespread usage in the robotics community. In this paper, we study the convergence of BTs, in the sense of reaching a desired part of the state space. Earlier results on BT convergence were often tailored to specific families of BTs, created using different design principles. The results of this paper generalize the earlier results and also include new cases of cyclic switching not covered in the literature.

eess.SY↗

arXiv2022

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. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.

cs.RO↗

arXiv2022

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. Machine learning approaches such as reinforcement learning or learning from demonstration can be very appealing to AI designers that want efficient and realistic behaviors in their agents. However, those algorithms seldom provide guarantees for solving the given task in all different situations while keeping the agent safe. Instead, such guarantees are often easier to find for manually designed model-based approaches. In this paper we exploit the modularity of behavior trees to extend a given design with an efficient, but possibly unreliable, machine learning component in a way that preserves the guarantees. The approach is illustrated with an inverted pendulum example.

cs.RO↗

arXiv2022

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. The key idea underlying behavior trees is to make use of modularity, hierarchies and feedback, in order to handle the complexity of a versatile robot control system. Modularity is a well-known tool to handle software complexity by enabling development, debugging and extension of separate modules without having detailed knowledge of the entire system. A hierarchy of such modules is natural, since robot tasks can often be decomposed into a hierarchy of sub-tasks. Finally, feedback control is a fundamental tool for handling uncertainties and disturbances in any low level control system, but in order to enable feedback control on the higher level, where one module decides what submodule to execute, information regarding progress and applicability of each submodule needs to be shared in the module interfaces. We will describe how these three concepts come to use in theoretical analysis, practical design, as well as extensions and combinations with other ideas from control theory and robotics.

cs.RO↗

arXiv2022

Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning

In this letter we show how to improve the performance of backward chained behavior trees (BTs) that use reinforcement learning (RL). BTs represent a hierarchical and modular way of combining control policies into higher level control policies. Backward chaining is a design principle for the construction of BTs that combine reactivity with goal directed actions in a structured way. The backward chained structure has also enabled convergence proofs for BTs, identifying a set of local conditions that lead to the convergence of all trajectories to a set of desired goal states. 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. In particular, previous analysis identified so-called active constraint conditions (ACCs), that should not be broken in order to avoid having to return to work on previously achieved subgoals. We propose a way to setup the RL problems, such that they do not only achieve each immediate subgoal, but also avoid violating the identified ACCs. The resulting performance improvement depends on how often ACC violations occurred before the change, and how much effort was needed to re-achieve them. The proposed approach is illustrated in a dynamic simulation environment.

eess.SY↗

arXiv2021

Continuous-Time Behavior Trees as Discontinuous Dynamical Systems

Behavior trees represent a hierarchical and modular way of combining several low-level control policies into a high-level task-switching policy. Hybrid dynamical systems can also be seen in terms of task switching between different policies, and therefore several comparisons between behavior trees and hybrid dynamical systems have been made, but only informally, and only in discrete time. A formal continuous-time formulation of behavior trees has been lacking. Additionally, convergence analyses of specific classes of behavior tree designs have been made, but not for general designs. 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. With these results, a large body of results on continuous-time dynamical systems can be brought to use when designing behavior tree controllers.

eess.SY↗

arXiv2021

On the similarities between Control Barrier Functions (CBFs) and Behavior Control Lyapunov Functions (BCLFs)

Control Barrier Functions (CBFs) is an important tool used to address situations with multiple concurrent control objectives, such as safety and goal convergence. In this paper we investigate the similarities between CBFs and so-called Behavior Control Lyapunov Functions (BCLFs) that have been proposed to address the same type of problems in the aeronautics domain. The key results of both CBFs and BCLFs is the description of the set of controls that render a given set invariant. We compare the corresponding theorems, and show that if we restrict the general class K function in CBFs to be the general linear function of BCLFs, and restrict the number of objectives as well as the number of priority levels to be just one in BCLFs, the results in terms of admissible control sets are equivalent. Furthermore, both papers show that the invariant set is made asymptotically stable.

cs.RO↗

arXiv2020

Learning Dynamic-Objective Policies from a Class of Optimal Trajectories

Optimal state-feedback controllers, capable of changing between different objective functions, are advantageous to systems in which unexpected situations may arise. However, synthesising such controllers, even for a single objective, is a demanding process. 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. We use numerical continuation to efficiently generate optimal demonstrations across several objectives and boundary conditions, and use these to train our policies. Additionally, we demonstrate the ability of our policies to effectively learn families of optimal state-feedback controllers, which can be used to change objective functions online. We illustrate this approach across two trajectory optimisation problems, an inverted pendulum swingup and a spacecraft orbit transfer, and show that the synthesised policies, when evaluated in simulation, produce trajectories that are near-optimal. These results indicate the benefit of trajectory optimisation and homotopy continuation to the synthesis of controllers in dynamic-objective contexts.

eess.SY↗

arXiv2020

Underwater Caging and Capture for Autonomous Underwater Vehicles

In this paper, we consider the problem of caging and eventual capture of an underwater entity using multiple Autonomous Underwater Vehicles (AUVs) in a 3D water volume We solve this problem both with and without taking bathymetry into account. Our proposed algorithm for range-limited sensing in 3D environments captures a finite-speed entity based on sparse and irregular observations. After an isolated initial sighting of the entity, the uncertainty of its whereabouts grows while deployment of the AUV system is underway. To contain the entity, an initial cage, or barrier of sensing footprints, is created around the initial sighting, using islands and other terrain as part of the cage if available. After the initial cage is established, the system waits for a second sighting, and the possible opportunity to create a smaller, shrinkable cage. This process continues until at some point it is possible to create this smaller cage, resulting in capture, meaning the entity is sensed directly and continuously. We present a set of algorithms for addressing the scenario above, and illustrate their performance on a set of examples. The proposed algorithm is a combination of solutions to the min-cut problem, the set cover problem, the linear bottleneck assignment problem and the Thomson problem.

cs.RO↗