Control Through Contact using Mixture of Deep Neural-Net Experts

Year 2025, Volume: 13 Issue: 2, 164 - 173

Aykut Satici

https://doi.org/10.17694/bajece.1515854

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.

Keywords

robotics, nonlinear control, machine learning

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