Certifiable Robot Design Optimization using Differentiable Programming

摘要

There is a growing need for computational tools to automatically design and verify autonomous systems, especially complex robotic systems involving perception, planning, control, and hardware in the autonomy stack. Differentiable programming has recently emerged as powerful tool for modeling and optimization. However, very few studies have been done to understand how differentiable programming can be used for robust, certifiable end-to-end design optimization. In this paper, we fill this gap by combining differentiable programming for robot design optimization with a novel statistical framework for certifying the robustness of optimized designs. Our framework can conduct end-to-end optimization and robustness certification for robotics systems, enabling simultaneous optimization of navigation, perception, planning, control, and hardware subsystems.