Large-scale robotics organizations operate diverse fleets of manipulators — 4-DOF arms on rails, 6-DOF standard manipulators on pedestals and fully mobile manipulators — each with different kinematics, hardware vendors, and software teams and researchers.
And engineers and researchers are opinionated.
As a team eventually you have one goal. Re-use as many abstractions; ideas and algorithms as you can while still allowing you the flexibility to explore new algorithms; ideas and approaches.
This talk presents a layered C++ architecture that solves this at scale. We present our core abstrcactions; a header-only Lie group library provides shared spatial math types (SE(3) poses, twists, wrenches); a trajectory library for motion planners and controllers; and a collision detection library from the same.
We will discuss how we moved from virtual functions to C++ concepts with zero overhead and the learnings along the way that allow us to write planning and control algorithms for various robots morphologies and sensing paradigms.
Critically, this architecture balances the needs of production at scale while enabling experimentation: researchers can prototype new collision algorithms, try a new physics engine, or test a novel trajectory representation — all without disrupting production code. The backend boundaries are where new ideas enter the system. You'll leave with three distinct C++ patterns for backend abstraction — compile-time traits, runtime interfaces, and type erasure — and an understanding of when to use each based on performance requirements and the need for experimentation.
