统一优化机器人程序参数与轨迹，兼顾效率与可解释性。
Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimization
- 用可微分规划构建运动规划器，支持梯度优化
- 在真实家庭与工业场景中实现周期时间与平滑性优化
- 输出结果可读可改，适合需安全验证的工程应用
本文提出SPI-DP，一种新型一阶优化器，可同时优化机器人程序的高层任务目标与运动级约束。为此，我们引入DGPMP2-ND，一个针对串联N自由度机械臂的可微分无碰撞运动规划器，并将其集成到迭代式梯度优化框架中，适用于通用参数化机器人程序表示。SPI-DP能够对规划轨迹与程序参数进行一阶优化，目标如周期时间或平滑性，同时满足碰撞约束，且优化结果具备可理解性、可修改性与可认证性。我们在两个实际家庭与工业应用场景中进行了全面评估。
原文摘要 · Abstract (English)
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.
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