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This paper introduces a novel approach for modeling the dynamics of softrobots, utilizing a differentiable filter architecture. The proposed approachenables end-to-end training to learn system dynamics, noise characteristics,and temporal behavior of the robot. A novel spatio-temporal embedding processis discussed to handle observations with varying sensor placements and samplingfrequencies. The efficacy of this approach is demonstrated on a tensegrityrobot arm by learning end-effector dynamics from demonstrations with complexbending motions. The model is proven to be robust against missing modalities,diverse sensor placement, and varying sampling rates. Additionally, theproposed framework is shown to identify physical interactions with humansduring motion. The utilization of a differentiable filter presents a novelsolution to the difficulties of modeling soft robot dynamics. Our approachshows substantial improvement in accuracy compared to state-of-the-artfiltering methods, with at least a 24% reduction in mean absolute error (MAE)observed. Furthermore, the predicted end-effector positions show an average MAEof 25.77mm from the ground truth, highlighting the advantage of our approach.The code is available at https://github.com/ir-lab/soft_robot_DEnKF.

Learning Soft Robot Dynamics using Differentiable Kalman Filters and Spatio-Temporal Embeddings