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pro vyhledávání: '"Bachhuber, Simon"'
In this paper, we extend the Recurrent Inertial Graph-based Estimator (RING), a novel neural-network-based solution for Inertial Motion Tracking (IMT), to generalize across a large range of sampling rates, and we demonstrate that it can overcome four
Externí odkaz:
http://arxiv.org/abs/2409.02502
Many application domains, e.g., in medicine and manufacturing, can greatly benefit from pneumatic Soft Robots (SRs). However, the accurate control of SRs has remained a significant challenge to date, mainly due to their nonlinear dynamics and viscoel
Externí odkaz:
http://arxiv.org/abs/2408.03754
Publikováno v:
Control Engineering Practice, Volume 145, April 2024, 105879
This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method that enables systems with unknown, nonlinear dynamics to autonomously learn to solve reference tracking tasks. The method iteratively applies an input trajectory to the unkno
Externí odkaz:
http://arxiv.org/abs/2404.06179