  Untethered soft robots that locomote using electrothermally-responsive
materials like shape memory alloy (SMA) face challenging design constraints for
sensing actuator states. At the same time, modeling of actuator behaviors faces
steep challenges, even with available sensor data, due to complex
electrical-thermal-mechanical interactions and hysteresis. This article
proposes a framework for in-situ sensing and dynamics modeling of actuator
states, particularly temperature of SMA wires, which is used to predict robot
motions. A planar soft limb is developed, actuated by a pair of SMA coils, that
includes compact and robust sensors for temperature and angular deflection.
Data from these sensors are used to train a neural network based on the long
short-term memory (LSTM) architecture to model both unidirectional (single SMA)
and bidirectional (both SMAs) motion. Predictions from the model demonstrate
that data from the temperature sensor, combined with control inputs, allow for
dynamics predictions over extraordinarily long open-loop timescales (10
minutes) with little drift. Prediction errors are on the order of the soft
deflection sensor's accuracy. This architecture allows for compact designs of
electrothermally-actuated soft robots that include sensing sufficient for
motion predictions, helping to bring these robots into practical application.
