  We present SymForce, a library for fast symbolic computation, code
generation, and nonlinear optimization for robotics applications like computer
vision, motion planning, and controls. SymForce combines the development speed
and flexibility of symbolic math with the performance of autogenerated, highly
optimized code in C++ or any target runtime language. SymForce provides
geometry and camera types, Lie group operations, and branchless singularity
handling for creating and analyzing complex symbolic expressions in Python,
built on top of SymPy. Generated functions can be integrated as factors into
our tangent-space nonlinear optimizer, which is highly optimized for real-time
production use. We introduce novel methods to automatically compute
tangent-space Jacobians, eliminating the need for bug-prone handwritten
derivatives. This workflow enables faster runtime code, faster development
time, and fewer lines of handwritten code versus the state-of-the-art. Our
experiments demonstrate that our approach can yield order of magnitude speedups
on computational tasks core to robotics. Code is available at
https://github.com/symforce-org/symforce.
