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.Comment: Submitted to IEEE Transactions on Game

Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics

We propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem. The proposed approach allows to handle partially observable initial states and improves the robustness of classical BTs against unexpected contingencies while at the same time reducing the number of nodes in a tree. In this work, the general nominal behavior is specified offline through BTs, where a new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as typically done in BTs. The decision of which action to execute to reach the desired state is performed online through active inference. This results in the combination of continual online planning and hierarchical deliberation, that is an agent is able to follow a predefined offline plan while still being able to locally adapt and take autonomous decisions at runtime. The properties of our algorithm, such as convergence and robustness, are thoroughly analyzed, and the theoretical results are validated in two different mobile manipulators performing similar tasks, both in a simulated and real retail environment

Synthesis of reactive control protocols for switch electrical power systems for commercial application with safety specifications

This paper presents a method for the reactive synthesis of fault-tolerant optimal control protocols for a finite deterministic discrete event system subject to safety specifications. A Deterministic Finite State Machine (DFSM) and Behavior Tree (BT) were used to model the system. The synthesis procedure involves formulating the policy problem as a shortest path dynamic programming problem. The procedure evaluates all possible states when applied to the DFSM, or over all possible actions when applied to the BT. The resulting strategy minimizes the number of actions performed to meet operational objectives without violating safety conditions. The effectiveness of the procedure on DFSMs and BTs is demonstrated through three examples of switched electrical power systems for commercial application and analyzed using run-time complexity analysis. The results demonstrated that for large order system BTs provided a tractable model to synthesize an optimal control policy

Simulating use cases for the UAH autonomous electric car

2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019This paper presents the simulation use cases for the UAH Autonomous Electric Car, related with typical driving scenarios in urban environments, focusing on the use of hierarchical interpreted binary Petri nets in order to implement the decision making framework of an autonomous electric vehicle. First, we describe our proposal of autonomous system architecture, which is based on the open source Robot Operating System (ROS) framework that allows the fusion of multiple sensors and the real-time processing and communication of multiple processes in different embedded processors. Then, the paper focuses on the study of some of the most interesting driving scenarios such as: stop, pedestrian crossing, Adaptive Cruise Control (ACC) and overtaking, illustrating both the executive module that carries out each behaviour based on Petri nets and the trajectory and linear velocity that allows to quantify the accuracy and robustness of the architecture proposal for environment perception, navigation and planning on a university Campus.Ministerio de Economía y CompetitividadComunidad de Madri