We introduce Learned Optimal Inverse Kinematics (LOInK), a method to generate approximately optimal solutions to an inverse kinematics problem. When trained on data consisting of sampled configurations and associated task variables and a given cost function, LOInK learns a bi-Lipschitz invertible mapping from configura...
Michael Somerfield, Damian Abood, Rui-Gang Wang et al.· 0 citations
This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of stable and robust recurrent neural networks for machine learning and data-driven control, and finds that training and inference are up to an order of magnitude faster with similar performance, and that they scale more favorably...
Nicholas H. Barbara, Rui-Gang Wang, I. Manchester· arXiv.org· 4 citations
The proposed robust invertibility framework is illustrated through a series of application examples in data-driven internal model control, dynamic surrogate loss learning, and signal-space normalizing flows, illustrating its utility for robust control, trajectory optimization, and generative modeling of complex traject...
Yurui Zhang, Ruigang Wang, I. Manchester· 0 citations
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