Abstract
To perform dynamic cable manipulation to realize the configuration specified by a target image, we formulate dynamic cable manipulation as a stochastic forward model. Then, we propose a method to handle uncertainty by maximizing the expectation, which also considers estimation errors of the trained model. To avoid issues like multiple local minima and requirement of differentiability by gradient-based methods, we propose using a black-box optimization (BBO) to optimize joint angles to realize a goal image. Among BBO, we use the Tree-structured Parzen Estimator (TPE), a type of Bayesian optimization. By incorporating constraints into the TPE, the optimized joint angles are constrained within the range of motion. Since TPE is population-based, it is better able to detect multiple feasible configurations using the estimated inverse model. We evaluated image similarity between the target and cable images captured by executing the robot using optimal transport distance. The results show that the proposed method improves accuracy compared to conventional gradient-based approaches and methods that use deterministic models that do not consider uncertainty.
Abstract (translated)
要执行动态电缆操纵以实现目标图像所指定的配置,我们将动态电缆操纵写成一个随机向前模型。然后,我们提出了一种方法,通过最大化期望来处理不确定性,并考虑训练模型的估计误差。为了避免类似多个局部最小值和基于梯度的方法要求的可变性要求等问题,我们提议使用黑盒优化(BBO)来优化连接角度以实现目标图像。在BBO中,我们使用树状分组估计器(TPE),这是一种贝叶斯优化的类型。通过将约束融入TPE中,优化的连接角度在运动范围内受到约束。由于TPE是基于人口的,因此它更好能够使用估计的逆模型来检测多个可行的配置。我们使用最优传输距离执行机器人来拍摄目标图像和电缆图像,并使用最佳传输距离评估它们之间的目标图像相似性。结果显示,与传统的基于梯度的方法和使用不考虑不确定性的确定性模型相比,提议的方法提高了精度。
URL
https://arxiv.org/abs/2301.11538
