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.

翻译：为实现目标图像所指定的构型，本文将动态缆索操控问题建模为随机前向模型。随后，提出一种通过最大化期望值处理不确定性的方法，该方法同时考虑了训练模型的估计误差。为避免梯度方法可能存在的多局部极小值及可微性需求问题，我们采用黑箱优化（BBO）方法优化关节角度以实现目标图像。在BBO方法中，选用树形结构的Parzen估计器（TPE），这是一种贝叶斯优化方法。通过将约束条件融入TPE，优化后的关节角度被限制在运动范围之内。由于TPE基于种群优化，其能借助估计的逆模型更好地检测多个可行构型。我们利用最优传输距离评估机器人执行操作后捕获的目标图像与缆索图像之间的相似度。结果表明，与传统梯度方法及不考虑不确定性的确定性模型方法相比，所提方法在精度上有所提升。