Reinforcement Learning for Control of a Shape Memory Alloy Based Self-Folding Sheet

Origami-inspired engineering provides engineers with new means for creating complicated three-dimensional structures through use of folding and fold-like operations. Motivated by the vision of origami engineering, we have created and modeled a reconfigurable self-folding sheet based on a laminate structure of shape memory alloy (SMA) surrounding a layer of elastomer. Folding behavior is achieved by activating an SMA layer through localized heating. In prior work, we demonstrated localized control of such a sheet using PID and On/Off type feedback controllers. The implementation of these control strategies requires several workarounds to deal with the highly nonlinear and hysteretic behavior of the SMA-based laminate sheet. In the current work, we use a reinforcement learning algorithm to learn control policies that better handle these aspects of the sheet behavior. We perform learning on a reduced order model of the sheet developed based on classical laminate plate theory. This significantly reduces computational costs compared to more complicated finite element modeling options. We demonstrate the effectiveness of the learned control policies in several folding scenarios on the reduced order model. Our results show that reinforcement learning can be a useful tool in feedback control of SMA-based structures.

Reinforcement Learning for Control of a Shape Memory Alloy Based Self-Folding Sheet Conference Paper