2,506 research outputs found
Neural networks robot controller trained with evolution strategies
Congress on Evolutionary Computation. Washington, DC, 6-9 July 1999.Neural networks (NN) can be used as controllers in autonomous robots. The specific features of the navigation problem in robotics make generation of good training sets for the NN difficult. An evolution strategy (ES) is introduced to learn the weights of the NN instead of the learning method of the network. The ES is used to learn high performance reactive behavior for navigation and collision avoidance. No subjective information about âhow to accomplish the taskâ has been included in the fitness function. The learned behaviors are able to solve the problem in different environments; therefore, the learning process has the proven ability to obtain a specialized behavior. All the behaviors obtained have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on the mini-robot, Khepera, has been used to learn each behavior
Evolving connection weights between sensors and actuators in robots
International Symposium on Industrial Electronics. Guimaraes, 7-11 July 1997.In this paper, an evolution strategy (ES) is introduced, to learn reactive behaviour in autonomous robots. An ES is used to learn high-performance reactive behaviour for navigation and collisions avoidance. The learned behaviour is able to solve the problem in a dynamic environment; so, the learning process has proven the ability to obtain generalised behaviours. The robot starts without information about the right associations between sensors and actuators, and, from this situation, the robot is able to learn, through experience, to reach the highest adaptability grade to the sensors information. No subjective information about âhow to accomplish the taskâ is included in the fitness function. A mini-robot Khepera has been used to test the learned behaviour
Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior
In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad
A general learning co-evolution method to generalize autonomous robot navigation behavior
Congress on Evolutionary Computation. La Jolla, CA, 16-19 July 2000.A new coevolutive method, called Uniform Coevolution, is introduced, to learn weights for a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collision avoidance. The coevolutive method allows the evolution of the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with or without coevolution have been tested in a set of environments and the capability for generalization has been shown for each learned behavior. A simulator based on the mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to example-based problems
Learning to Fly by Crashing
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real world remains large especially for perception problems. The
reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes
itself! We build a drone whose sole purpose is to crash into objects: it
samples naive trajectories and crashes into random objects. We crash our drone
11,500 times to create one of the biggest UAV crash dataset. This dataset
captures the different ways in which a UAV can crash. We use all this negative
flying data in conjunction with positive data sampled from the same
trajectories to learn a simple yet powerful policy for UAV navigation. We show
that this simple self-supervised model is quite effective in navigating the UAV
even in extremely cluttered environments with dynamic obstacles including
humans. For supplementary video see: https://youtu.be/u151hJaGKU
End-to-end Driving via Conditional Imitation Learning
Deep networks trained on demonstrations of human driving have learned to
follow roads and avoid obstacles. However, driving policies trained via
imitation learning cannot be controlled at test time. A vehicle trained
end-to-end to imitate an expert cannot be guided to take a specific turn at an
upcoming intersection. This limits the utility of such systems. We propose to
condition imitation learning on high-level command input. At test time, the
learned driving policy functions as a chauffeur that handles sensorimotor
coordination but continues to respond to navigational commands. We evaluate
different architectures for conditional imitation learning in vision-based
driving. We conduct experiments in realistic three-dimensional simulations of
urban driving and on a 1/5 scale robotic truck that is trained to drive in a
residential area. Both systems drive based on visual input yet remain
responsive to high-level navigational commands. The supplementary video can be
viewed at https://youtu.be/cFtnflNe5fMComment: Published at the International Conference on Robotics and Automation
(ICRA), 201
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