Learning Soft Robot Dynamics Using Differentiable Kalman Filters and Spatio-Temporal Embeddings
Xiao Liu,Shuhei Ikemoto,Yuhei Yoshimitsu,Heni Ben Amor,Xiao Liu,Shuhei Ikemoto,Yuhei Yoshimitsu,Heni Ben Amor
This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a differentiable filter architecture. The proposed approach enables end-to-end training to learn system dynamics, noise characteristics, and temporal behavior of the robot. A novel spatio-temporal embedding process is discussed to handle observations with varying sensor placements and sampling frequencies. T...
