Accurate Prediction of Ball Trajectories with Spin Using Differentiable Factor Graph and Roto-Translational Invariant Representations

An end-to-end learning framework that jointly trains a dynamics model and a factor graph estimator, leveraging roto-translational invariant representations and a self-multiplicative neural network architecture, to accurately predict ball trajectories with various types of spin.