Predicting soft robot's locomotion fitness (2021)
- Authors:
- USP affiliated authors: FUJITA, ANDRÉ - IME ; BIAZZI, RENATA BIAGGI - Interunidades em Bioinformática
- Unidades: IME; Interunidades em Bioinformática
- DOI: 10.1145/3449726.3459417
- Subjects: ROBÓTICA; LOCOMOÇÃO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: Genetic and Evolutionary Computation Conference Companion - GECCO
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
BIAZZI, Renata Biaggi e FUJITA, André e TAKAHASHI, Daniel Yasumasa. Predicting soft robot's locomotion fitness. 2021, Anais.. New York: ACM, 2021. Disponível em: https://doi.org/10.1145/3449726.3459417. Acesso em: 08 abr. 2026. -
APA
Biazzi, R. B., Fujita, A., & Takahashi, D. Y. (2021). Predicting soft robot's locomotion fitness. In Proceedings. New York: ACM. doi:10.1145/3449726.3459417 -
NLM
Biazzi RB, Fujita A, Takahashi DY. Predicting soft robot's locomotion fitness [Internet]. Proceedings. 2021 ;[citado 2026 abr. 08 ] Available from: https://doi.org/10.1145/3449726.3459417 -
Vancouver
Biazzi RB, Fujita A, Takahashi DY. Predicting soft robot's locomotion fitness [Internet]. Proceedings. 2021 ;[citado 2026 abr. 08 ] Available from: https://doi.org/10.1145/3449726.3459417 - Convergent evolution in silico reveals shape and dynamic principles of directed locomotion on the ground
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