Evolving soft robotic locomotion in PhysX

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Evolving soft robotic locomotion in PhysX

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers table of contents

Montreal, Québec, Canada

WORKSHOP SESSION: Computational intelligence on consumer games and graphics hardware (CIGPU) 2009 table of contents

Pages 2499-2504

Year of Publication: 2009

ISBN:978-1-60558-505-5

Authors

John Rieffel	Tufts University, Medford, MA, USA
Frank Saunders	Tufts University, Medford, MA, USA
Shilpa Nadimpalli	Tufts University, Medford, MA, USA
Harvey Zhou	Tufts University, Medford, MA, USA
Soha Hassoun	Tufts University, Medford, MA, USA
Jason Rife	Tufts University, Medford, MA, USA
Barry Trimmer	Tufts University, Medford, MA, USA

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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