Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimization

Abstract-- This paper presents Shadow Program Inversion with Differentiable Planning (SPI-DP), a novel first-order optimizer capable of optimizing robot programs with respect to both high-level task objectives and motion-level constraints. To that end, we introduce Differentiable Gaussian Process Motion Planning for N-DoF Manipulators (dGPMP2-ND), a differentiable collision-free motion planner for serial N-DoF kinematics, and integrate it into an iterative, gradient-based optimization approach for generic, parameterized robot program representations. SPI-DP allows first-order optimization of planned trajectories and program parameters with respect to objectives such as cycle time or smoothness subject to e.g. However, the cost of automation is often driven by the iterative optimization of with respect to task-and motion-level constraints. We make robot trajectories and program parameters, particularly for the following contributions: complex manipulation tasks.