Abstract
Creating robust robot platforms that function in the real world is a difficult task. Adding the requirement that the platform should be capable of learning, from nothing, ways to generate its own movement makes the task even harder. Evolutionary Robotics is a promising field that combines the creativity of evolutionary optimization with the real-world focus of robotics to bring about unexpected control mechanisms in addition to whole new robot designs. Constructing a platform that is capable of these feats is difficult, and it is important to share experiences and lessons learned so that designers of future robot platforms can benefit. In this paper, we introduce our robotics platform and detail our experiences with real-world evolution. We present thoughts on initial design considerations and key insights we have learned from extensive experimentation. We hope to inspire new platform development and hopefully reduce the threshold of doing real-world legged robot evolution.
This work is partially supported by The Research Council of Norway under grant agreement 240862 and through its Centers of Excellence scheme, project number 262762. Simulations with DyRET were performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.
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Nygaard, T.F., Nordmoen, J., Ellefsen, K.O., Martin, C.P., Tørresen, J., Glette, K. (2019). Experiences from Real-World Evolution with DyRET: Dynamic Robot for Embodied Testing. In: Bach, K., Ruocco, M. (eds) Nordic Artificial Intelligence Research and Development. NAIS 2019. Communications in Computer and Information Science, vol 1056. Springer, Cham. https://doi.org/10.1007/978-3-030-35664-4_6
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