Art Boyarov*, Zichen Zhang*.
We study the problem of learning the pushing dynamics of arbitrary 2D rigid bodies, developing neural network models trained on simulated data collected with a Franka Panda robot. By comparing a shallow MLP to a deeper point-cloud-inspired network, we show that the deeper model better captures the complex motion dynamics of different 2D shapes. Using the learned models in a Model Predictive Path Integral (MPPI) controller, we successfully achieve closed-loop pushing and obstacle avoidance across diverse 2D rigid bodies.
