Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimization
This work addresses the challenge of unified robot program optimization for household and industrial applications, representing an incremental advancement by integrating differentiable planning into existing optimization methods.
The paper tackles the problem of optimizing robot programs for both high-level task objectives and motion-level constraints, resulting in a framework that enables gradient-based optimization of planned trajectories and program parameters with respect to objectives like cycle time or smoothness while ensuring collision constraints.
This paper presents 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 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. collision constraints, while enabling humans to understand, modify or even certify the optimized programs. We provide a comprehensive evaluation on two practical household and industrial applications.