Contact DynamicsĬontact dynamics are a class of equations which describe the motion of physical systems consisting of multiple solid objects which interact with each other and their environment. In this work, we explore neural network architectures designed for accurately modeling contact dynamics, which incorporate the structure necessary to reliably resolve non-smooth collision events. Such behavior is of key interest in robotics, and other areas of engineering. These network classes exhibit good approximation behavior for continuous physical systems but they are fundamentally limited to smooth dynamics, and not designed to handle non-smooth physical behavior, such as resolving collision events between different objects. To counteract this, a number of recent works such as Hamiltonian Neural Networks and Variational Integrator Networks introduce inductive biases, also referred to as physics priors, which improve reliability of predictions and speed up learning. Vanilla neural networks-like residual networks-particularly struggle to learn invariant properties like the conservation of energy which is fundamental to physical systems. Learning models of physical systems can sometimes be difficult.
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