Deep Segmented DMP Networks for Learning Discontinuous Motions

TL;DR

This paper introduces DSDNet, a novel deep learning architecture that effectively generates complex, discontinuous robotic motions by segmenting and predicting multiple DMP parameters, improving generalization and data efficiency.

Contribution

The paper proposes DSDNet, a new deep learning framework that enhances DMP-based motion generation for complex discontinuous tasks with variable-length segmentation.

Findings

High generalization capability on artificial and real data

Effective generation of discontinuous long-horizon motions

Improved data efficiency over previous methods

Abstract

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.