Differentiable physics models enable learning contact dynamics for robotic systems, but at what cost? The underlying stiffness of contact poses a fundamental challenge to deep learning methods. Via numerical experiments learning ODEs for contact dynamics, we find that stiffness severely impacts (1) training error, (2) generalization error, and (3) data efficiency.
The theoretical underpinnings of these results are perhaps well known, arising from the high Lipschitz constants due to contact stiffness. However, given the rise of deep learning applied to differentiable physics models of contact, it’s important to keep in mind the limitations of these approximations. There’s a resulting fundamental trade-off between physical accuracy (for stiff robotic contact) and amenability to learning methods.
Learning on artificially soft contact models may not transfer to stiffer, real systems!