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:为了进行动态电缆操纵以实现目标图像指定的配置, 我们设计了动态电缆操作, 作为一种随机的前方模型。 然后, 我们提出一种方法, 通过尽量扩大预期来应对不确定性, 同时考虑经过训练的模式的估计错误。 为了避免多重本地迷你和基于梯度的方法要求差异性的问题, 我们提议使用黑盒优化( BBO) 优化联合角度来实现目标图像。 在BBO 中, 我们使用树型Parzen Estimator( TPE), 这是一种巴耶西亚优化。 通过将限制纳入 TPE, 优化的联合角度在运动范围内受到限制。 由于TPE 以人口为基础, 它更有能力利用估计的反向模型探测多种可行的配置。 我们用最佳运输距离执行机器人所捕捉到的目标与电缆图像之间的图像相似性。 结果显示, 与传统梯度方法相比, 一种使用不考虑不确定性的确定性模型的方法, 提高了准确性。