Autotuning Symbolic Optimization Fabrics for Trajectory Generation

TL;DR

This paper introduces an automated parameter tuning framework for trajectory generation using Bayesian optimization, demonstrating its effectiveness on symbolic fabrics and its transferability across robots and environments.

Contribution

It presents a novel autotuning method for symbolic trajectory fabrics, enabling rapid, cross-domain optimization with minimal trials.

Findings

Autotuned symbolic fabrics achieve expert-level performance in few trials.

Parameter tuning transfers effectively across different robots and environments.

Framework can be extended to coupled mobile manipulation tasks.

Abstract

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.