Pulsed Neural Networks
The X2 modular evolutionary robotics platform
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
A review of gait optimization based on evolutionary computation
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
Evolving locomotion for a simulated 12-DOF quadruped robot
IPCAT'12 Proceedings of the 9th international conference on Information Processing in Cells and Tissues
A comparison of sampling strategies for parameter estimation of a robot simulator
SIMPAR'12 Proceedings of the Third international conference on Simulation, Modeling, and Programming for Autonomous Robots
Study on evolution of the artificial flying creature controlled by neuro-evolution
Artificial Life and Robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Communications of the ACM
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Given the complexity of the problem, genetic algorithms are one of the more promising methods of discovering control schemes for soft robotics. Since physically embodied evolution is time consuming and expensive, an outstanding challenge lies in developing fast and suitably realistic simulations in which to evolve soft robot gaits. We describe two parallel methods of using NVidia's PhysX, a hardware-accelerated (GPGPU) physics engine, in order to evolve and optimize soft bodied gaits. The first method involves the evolution of open-loop gaits using a reduced-order lumped parameter model. The second method involves harnessing PhysX's soft-bodied material simulation capabilites. In each case we discuss the the challenges and possibilities involved in using the PhysX for evolutionary soft robotics.