@article{article_1515854, title={Control Through Contact using Mixture of Deep Neural-Net Experts}, journal={Balkan Journal of Electrical and Computer Engineering}, volume={13}, pages={164–173}, year={2025}, DOI={10.17694/bajece.1515854}, author={Satici, Aykut}, keywords={robotics, nonlinear control, machine learning}, abstract={We provide a data-driven control design framework
for hybrid systems, with a special emphasis on contact-rich
robotic systems. These systems exhibit continuous state flows
and discrete state transitions, which are governed by distinct
equations of motion. Hence, it may be impossible to design a
single policy that can control the system in all modes. Typically,
hybrid systems are controlled by multi-modal policies, each
manually triggered based on observed states. However, as the
number of potential contacts increase, the number of policies can
grow exponentially and the control-switching scheme becomes
too complicated to parameterize. To address this issue, we
design contact-aware data-driven controllers given by deepnet
mixture of experts (MoE). This architecture automatically
learns switching-control scheme that can achieve the desired
overall performance of the system, and a gating network, which
determines the region of validity of each expert, based on the
observed states.}, number={2}, publisher={MUSA YILMAZ}