In this work we present CppFlow - a novel and performant planner for the Cartesian Path Planning problem, which finds valid trajectories up to 129x faster than current methods, while also succeeding on more difficult problems where others fail. At the core of the proposed algorithm is the use of a learned, generative Inverse Kinematics solver, which is able to efficiently produce promising entire candidate solution trajectories on the GPU. Precise, valid solutions are then found through classical approaches such as differentiable programming, global search, and optimization. In combining approaches from these two paradigms we get the best of both worlds - efficient approximate solutions from generative AI which are made exact using the guarantees of traditional planning and optimization. We evaluate our system against other state of the art methods on a set of established baselines as well as new ones introduced in this work and find that our method significantly outperforms others in terms of the time to find a valid solution and planning success rate, and performs comparably in terms of trajectory length over time. The work is made open source and available for use upon acceptance.

翻译：摘要：本文提出CppFlow——一种面向笛卡尔路径规划问题的新型高性能规划器，其寻找有效轨迹的速度相比现有方法最高提升129倍，同时在更困难的问题上以更高成功率完成规划。该算法的核心在于采用学习型生成式逆运动学求解器，能够在GPU上高效生成完整的候选解轨迹。随后通过可微编程、全局搜索和优化等经典方法获得精确可行解。通过融合两类范式的优势，我们实现了兼顾效率与准确性的双重效益——生成式AI提供的高效近似解经传统规划与优化的确定性保证后达到精确解标准。在既有基准测试及本文新引入的测试集上，我们将本系统与多种前沿方法进行对比，结果表明本方法在求解时间和规划成功率上显著优于其他方法，在轨迹长度与规划时间方面表现相当。本工作将在接收后开源供学界使用。