Data-driven Model Reduction for Soft Robots via Lagrangian Operator Inference

Autor: Sharma, Harsh, Adibnazari, Iman, Cervera-Torralba, Jacobo, Tolley, Michael T., Kramer, Boris

Data-driven model reduction methods provide a nonintrusive way of constructing computationally efficient surrogates of high-fidelity models for real-time control of soft robots. This work leverages the Lagrangian nature of the model equations to deri

Externí odkaz: http://arxiv.org/abs/2407.08840

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Branching Vine Robots for Unmapped Environments.

Autor: Glick PE; Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States.; NASA Jet Propulsion Laboratory (JPL), La Cañada Flintridge, CA, United States., Adibnazari I; Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States., Drotman D; Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States., Ruffatto Iii D; NASA Jet Propulsion Laboratory (JPL), La Cañada Flintridge, CA, United States., Tolley MT; Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, United States.

Publikováno v: Frontiers in robotics and AI [Front Robot AI] 2022 Mar 24; Vol. 9, pp. 838913. Date of Electronic Publication: 2022 Mar 24 (Print Publication: 2022).