Abstract

Shape Memory Alloy (SMA) actuated soft robotic systems offer advantages in adaptability and compliance but present significant challenges for modeling and control due to nonlinear and time-dependent behavior. This work presents a data-driven approach for estimating the three-dimensional position of SMA-actuated soft robotic limbs using recurrent neural networks (RNNs). A multi-limbed soft robotic platform equipped with embedded temperature, current, and inertial measurement sensors was developed, with motion-capture data providing ground-truth position labels. A randomized actuation algorithm enabled varied and continuous data collection across independent and coupled limb activations. Advanced Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures with residual connections and layer normalization were trained and evaluated using synchronized multimodal sensor data …

Date

February 19, 2026

Authors

Joshua Pastizzo, Griffin MacRae, Mira Oflus, Kristina Andreyeva, David Barnhart

Book

AIAA SCITECH 2026 Forum

Pages

1036