The evolution of size and shape
Advances in genetic programming
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Evolving neural networks through augmenting topologies
Evolutionary Computation
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics
Proceedings of the Third European Conference on Advances in Artificial Life
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A Taxonomy for artificial embryogeny
Artificial Life
Flying over the reality gap: From simulated to real indoor airships
Autonomous Robots
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Combining Simulation and Reality in Evolutionary Robotics
Journal of Intelligent and Robotic Systems
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Evolving mobile robots in simulated and real environments
Artificial Life
Combining Multiple Inputs in HyperNEAT Mobile Agent Controller
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Guiding single-objective optimization using multi-objective methods
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Crossing the reality gap in evolutionary robotics by promoting transferable controllers
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Neuroevolution of mobile ad hoc networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
HyperNEAT for locomotion control in modular robots
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
Evolving a single scalable controller for an octopus arm with a variable number of segments
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
How to promote generalisation in evolutionary robotics: the ProGAb approach
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
On the Performance of Indirect Encoding Across the Continuum of Regularity
IEEE Transactions on Evolutionary Computation
Multirobot behavior synchronization through direct neural network communication
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
Hi-index | 0.00 |
The robustness of animal behavior is unmatched by current machines, which often falter when exposed to unforeseen conditions. While animals are notably reactive to changes in their environment, machines often follow finely tuned yet inflexible plans. Thus, instead of the traditional approach of training such machines over many different unpredictable scenarios in detailed simulations (which is the most intuitive approach to inducing robustness), this work proposes to train machines to be reactive to their environment. The idea is that robustness may result not from detailed internal models or finely tuned control policies but from cautious exploratory behavior. Supporting this hypothesis, robots trained to navigate mazes with a reactive disposition prove more robust than those trained over many trials yet not rewarded for reactive behavior in both simulated tests and when embodied in real robots. The conclusion is that robustness may neither require an accurate model nor finely calibrated behavior.