GA-based learning in behaviour based robotics

Computational Intelligence in Control

In this chapter an evolutionary algorithm is developed to learn a fuzzy knowledge base for the control of a soccer micro-robot from any configuration belonging to a grid of initial configurations, to hit the ball along the ball to goal line of sight. A relative coordinate system is used. Forward and reverse mode of the robot and its physical dimensions are incorporated, as well as special considerations to cases when in its initial configuration, the robot is touching the ball.

1996

The implementation of behaviors for embodied autonomous agents by means of Fuzzy Logic Controllers (FLC) has natural and engineering motivations. Fuzzy logic is recognized as a powerful mean to represent approximation intrinsic in human (and animal) reasoning and reacting. On the other side, fuzzy logic shows flexibility and robustness, important in the implementation of artificial devices. Two aspects of the development of autonomous agents may be faced by learning FLCs: the adaptation of the agent to the environment, and the reduction of the design time and efforts. In this paper, we present issues related to learn behaviors implemented as FLCs, and we propose our approach implemented in ELF (Evolutionary Learning for Fuzzy rules). We are using ELF to support the development of different types of agents. Finally, we present the results that we have obtained both in simulated and real environments.

International Journal of Intelligent Systems, 2005

This article presents a hybrid learning architecture for fuzzy control of quadruped walking robots in the RoboCup domain. It combines reactive behaviors with deliberative reasoning to achieve complex goals in uncertain and dynamic environments. To achieve real-time and robust control performance, fuzzy logic controllers (FLCs) are used to encode the behaviors and a two-stage learning scheme is adopted to make these FLCs be adaptive to complex situations. The first stage is called structure learning, in which the rule base of an FLC is generated by a Q-learning scheme. The second stage is called parameter learning, in which the parameters of membership functions in input fuzzy sets are learned by using a real value genetic algorithm. The experimental results are provided to show the suitability of the architecture and effectiveness of the proposed learning scheme.

Robotics and Autonomous Systems, 1998

This paper concerns tdae learning of basic behaviors in an autonomous robot. It presents a method to adapt basic reactive behaviors using a genetic algorithm. Behaviors are implemented as fuzzy controllers and the genetic algorithm is used to evolve their rules. These rules will be formulated in a fuzzy way using prefixed linguistic labels. In order to test the rules obtained in each generation of the genetic evolution process, a real robot has been used. Numerical results from the evolution rate of the different experiments, as well as an example of the fuzzy rules obtained, are presented and discussed.

International Journal of Approximate …, 1997

Fuzzy Logic Controllers constitute knowledge-based systems that include Fuzzy Rules and Fuzzy Membership Functions to incorporate human knowledge into their knowledge base. The de nition of fuzzy rules and fuzzy membership functions is one of the key question when designing Fuzzy Logic Controllers, and is generally a ected by subjective decisions. Some e orts have been made to obtain an improvement on system performance by incorporating learning mechanisms to modify the rules and/or membership functions of the FLC. Genetic Algorithms are probabilistic search and optimization procedures based on natural genetics. This paper proposes a way to apply (with a learning purpose) Genetic Algorithms to Fuzzy Logic Controllers, and presents an application designed to control the Synthesis of biped walk of a simulated 2-D biped robot.

2012

This paper is concerned with the learning of basic behaviors in autonomous robots. In this way, we present a method for the adaptation of basic reactive behaviors implemented as fuzzy controllers applying a genetic algorithm to the evolution of the fuzzy rule system. In this sense, we show our experiments in the evolution of control rules based on symbolic concepts represented as linguistic labels. The rules will be formulated in a fuzzy way and in order to test the rules obtained in each generation of the genetic algorithm a real robot has been used. The individual with the best performance is chosen to generate a new population: the elite strategy. All the new individuals were tested in the same real environment. In conclusion, the individuals of the last generation offer a set of rules that provides better performance than the ones designed by a non-expert designer.

Proceedings of the 15th IFAC World Congress, 2002, 2002

This paper presents a fuzzy logic controller (FLC) for the implementation of some behaviour of Sony legged robots. The adaptive heuristic Critic (AHC) reinforcement learning is employed to refine the FLC. The actor part of AHC is a conventional FLC in which the parameters of input membership functions are learned by an immediate internal reinforcement signal. This internal reinforcement signal comes from a prediction of the evaluation value of a policy and the external reinforcement signal. The evaluation value of a policy is learned by temporal difference (TD) learning in the critic part that is also represented by a FLC. A genetic algorithm (GA) is employed for learning internal reinforcement of the actor part because it is more efficient in searching than other trial and error search approaches.

IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2000

This paper presents the design and implementation of a coordination architecture for quadruped walking robots to learn and execute soccer-playing behaviors. A typical hybrid architecture combing reactive behaviors with deliberative reasoning is developed. The reactive behaviors directly map spatial information extracted from sensors into actions. The deliberative reasoning represents temporal constraints of a robot's strategy in terms of finite state machines. In order to achieve real-time and robust control performance in reactive behaviors, fuzzy logic controllers (FLCs) are used to encode the behaviors, and a two-stage learning scheme is adopted to make these FLCs adaptive to complex situations. The experimental results are provided to show the suitability of the architecture and effectiveness of the proposed learning scheme.

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

In this contribution we propose a hybrid genetic programming approach for evolving a decision making system in the domain of RoboCup Soccer (Simulation League). Genetic programming has been rarely used in this domain in the past, due to the di culties and restrictions of the soccer simulation. e real-time requirements of robot soccer and the lengthy evaluation time even for simulated games provide a formidable obstacle to the application of evolutionary approaches. Our new method uses two evolutionary phases, each of which compensating for restrictions and limitations of the other. e rst phase produces some evolved GP individuals applying an o-game evaluation system which can be trained on snapshots of game situations as they actually happened in earlier games, and corresponding decisions tagged as correct or wrong. e second phase uses the best individuals of the rst phase as input to run another GP system to evolve players in a real game environment where the quality of decisions is evaluated through winning or losing during real-time runs of the simulator. We benchmark the new system against a baseline system used by most simulation league teams, as well as against winning systems of the 2016 tournament. CCS CONCEPTS •Computer systems organization → Evolutionary robotics; •So ware and its engineering → Genetic programming;

Robotics and Autonomous Systems, 2007

In a multi-robotic system, robots interact with each other in a dynamically changing environment. The robots need to be intelligent both at the individual and group levels. In this paper, the evolution of a fuzzy behavior-based architecture is discussed. The behavior-based architecture decomposes the complicated interactions of multiple robots into modular behaviors at different complexity levels. The fuzzy logic approach brings in human-like reasoning to the behavior construction, selection and coordination. Various behaviors in the fuzzy behavior-based architecture are evolved by genetic algorithm (GA). At the lowest level of the architecture hierarchy, the evolved fuzzy controllers enhanced the smoothness and accuracy of the primitive robot actions. At a higher level, the individual robot behaviors have become more skillful after the evolution. At the topmost level, the evolved group behaviors have resulted in aggressive competition strategy. The simulation and real-world experimentation on a robot-soccer system justify the effectiveness of the approach.