Abstract

Soft robotic systems present substantial challenges for state estimation due to their nonlinear actuation dynamics, material hysteresis, and underactuated geometries. These challenges are especially pronounced in Shape Memory Alloy (SMA) driven platforms, where mechanical motion is coupled with thermoelectric behavior that varies over time. Learning-based models such as Gated Recurrent Units (GRUs) have demonstrated strong predictive performance for soft robotic state estimation, but the role of individual sensor inputs is often unclear. This work investigates sensor contribution and redundancy within a GRU-based forward model trained to estimate the three-dimensional position of an SMA-actuated soft robotic limb. A structured ablation framework is employed to train separate models with specific sensor modalities omitted from the input data, including current, infrared temperature, and inertial …

Date

January 30, 2026

Authors

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

Book

AIAA SCITECH 2026 Forum

Pages

0430