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Evolving Soft Robotic Locomotion in PhysX

John Rieffel, Frank Saunders, Shilpa Nadimpalli, Harvey Zhou, Soha Hassoun, Jason Rife, Barry Trimmer
Biomimetic Devices Lab, Tufts University, Medford, MA, USA
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, GECCO ’09

@conference{rieffel2009evolving,

   title={Evolving soft robotic locomotion in PhysX},

   author={Rieffel, J. and Saunders, F. and Nadimpalli, S. and Zhou, H. and Hassoun, S. and Rife, J. and Trimmer, B.},

   booktitle={Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers},

   pages={2499–2504},

   year={2009},

   organization={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.
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