Control Through Contact using Mixture of Deep Neural-Net Experts

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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