A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data
Kun Wang,Mridul Aanjaneya,Kostas Bekris,Kun Wang,Mridul Aanjaneya,Kostas Bekris
Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines have been recently proposed as a data-driven approach for model identification of such complex robotic systems. These engines are often executed at a high-frequency to achieve accurate simulation....
