Intelligence without representation
Artificial Intelligence
Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An Behavior-based Robotics
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Evolving neural networks through augmenting topologies
Evolutionary Computation
Learning Deterministic Finite Automata with a Smart State Labeling Evolutionary Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments
Evolutionary Computation
Evolving a real-world vehicle warning system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Active Coevolutionary Learning of Deterministic Finite Automata
The Journal of Machine Learning Research
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Fitness functions in evolutionary robotics: A survey and analysis
Robotics and Autonomous Systems
Learning complex motions by sequencing simpler motion templates
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Refining the execution of abstract actions with learned action models
Journal of Artificial Intelligence Research
Pyevolve: a Python open-source framework for genetic algorithms
ACM SIGEVOlution
Action-related place-based mobile manipulation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning situation dependent success rates of actions in a RoboCup scenario
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Towards performing everyday manipulation activities
Robotics and Autonomous Systems
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We propose a robotics algorithm that is able to simultaneously combine, adapt and create actions to solve a task. The actions are combined in a Finite State Automaton whose structure is determined by a novel evolutionary algorithm. The actions parameters, or new actions, are evolved alongside the FSA topology. Actions can be combined together in a hierarchical fashion. This approach relies on skills that with which the robot is already provided, like grasping or motion planning. Therefore software reuse is an important advantage of our proposed approach. We conducted several experiments both in simulation and on a real mobile manipulator PR2 robot, where skills of increasing complexity are evolved. Our results show that (i) an FSA generated in simulation can be directly applied to a real robot without modifications and (ii) the evolved FSA is robust to the noise and the uncertainty arising from real-world sensors.