In this paper, we present an automated parameter optimization method for trajectory generation. We formulate parameter optimization as a constrained optimization problem that can be effectively solved using Bayesian optimization. While the approach is generic to any trajectory generation method, we showcase it using optimization fabrics. Optimization fabrics are a geometric trajectory generation method based on non-Riemannian geometry. By symbolically pre-solving the structure of the tree of fabrics, we obtain a parameterized trajectory generator, called symbolic fabrics. We show that autotuned symbolic fabrics reach expert-level performance in a few trials. Additionally, we show that tuning transfers across different robots, motion planning problems and between simulation and real world. Finally, we qualitatively showcase that the framework could be used for coupled mobile manipulation.

翻译：本文提出了一种用于轨迹生成的自动化参数优化方法。我们将参数优化形式化为一个约束优化问题，并利用贝叶斯优化有效求解。虽该方法具有通用性，可适用于任意轨迹生成方法，但本文以优化织物为例进行展示。优化织物是一种基于非黎曼几何的几何轨迹生成方法。通过符号化预求解织物树结构，我们获得了一个参数化的轨迹生成器，称为符号化织物。实验表明，自调优符号化织物在少量试验中即可达到专家级性能。此外，我们证明了该调优方法可在不同机器人、运动规划问题以及仿真与真实世界之间迁移。最后，我们定性展示了该框架可用于耦合移动操作任务。