SymForce: Symbolic Computation and Code Generation for Robotics

TL;DR

SymForce is a library that combines symbolic computation with code generation to optimize robotics algorithms, significantly improving runtime performance and development efficiency.

Contribution

It introduces a novel workflow that automates tangent-space Jacobian computation and generates highly optimized code for robotics applications.

Findings

Achieves order of magnitude speedups in core robotics computations

Reduces development time and handwritten code through automation

Provides a flexible Python interface with high-performance C++ code generation

Abstract

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.