Discontinuous motion which is a motion composed of multiple continuous motions with sudden change in direction or velocity in between, can be seen in state-aware robotic tasks. Such robotic tasks are often coordinated with sensor information such as image. In recent years, Dynamic Movement Primitives (DMP) which is a method for generating motor behaviors suitable for robotics has garnered several deep learning based improvements to allow associations between sensor information and DMP parameters. While the implementation of deep learning framework does improve upon DMP's inability to directly associate to an input, we found that it has difficulty learning DMP parameters for complex motion which requires large number of basis functions to reconstruct. In this paper we propose a novel deep learning network architecture called Deep Segmented DMP Network (DSDNet) which generates variable-length segmented motion by utilizing the combination of multiple DMP parameters predicting network architecture, double-stage decoder network, and number of segments predictor. The proposed method is evaluated on both artificial data (object cutting & pick-and-place) and real data (object cutting) where our proposed method could achieve high generalization capability, task-achievement, and data-efficiency compared to previous method on generating discontinuous long-horizon motions.

翻译：非连续运动是由多个连续运动组成、其间方向或速度发生突变的运动形式，常见于具有状态感知能力的机器人任务中。此类机器人任务通常需要与图像等传感器信息协同配合。近年来，动态运动基元作为生成适用于机器人运动行为的方法，已衍生出多项基于深度学习的改进方案，以实现传感器信息与DMP参数的关联。尽管深度学习框架的引入改善了DMP直接关联输入能力的缺失，但我们发现，对于需要大量基函数重构的复杂运动，现有方法在预测DMP参数时存在困难。本文提出一种新型深度学习网络架构——深度分段DMP网络，该网络通过结合多DMP参数预测网络架构、双阶段解码器网络以及分段数量预测器，能够生成可变长度的分段运动。我们在人工数据（物体切割与抓取放置）和真实数据（物体切割）上评估了该方法，结果表明，与现有生成非连续长时域运动的方法相比，本方法具有更强的泛化能力、任务完成能力与数据效率。