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.
|Title of host publication||Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2023)|
|Publication status||Published - 2023|
|Event||ICRA 2023: International Conference on Robotics and Automation - London, United Kingdom|
Duration: 29 May 2023 → 2 Jun 2023
|Conference||ICRA 2023: International Conference on Robotics and Automation|
|Period||29/05/23 → 2/06/23|
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