Learning cooperative dynamic manipulation skills from human demonstration videos
- Title
- Learning cooperative dynamic manipulation skills from human demonstration videos
- Author
- 김완수
- Keywords
- Transfer Learning; Multi-Agent Systems; 3D Pose Estimation; Visual Imitation; Human Action
- Issue Date
- 2022-04
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Citation
- MECHATRONICS, Page. 102807-102807
- Abstract
- This article proposes a method for learning and robotic replication of dynamic collaborative tasks from offline videos. The objective
is to extend the concept of learning from demonstration (LfD) to dynamic scenarios, benefiting from widely available or easily
producible offline videos. To achieve this goal, we decode important dynamic information, such as the Configuration Dependent
Stiffness (CDS), which reveals the contribution of arm pose to the arm endpoint stiffness, from a three-dimensional human skeleton
model. Next, through encoding of the CDS via Gaussian Mixture Model (GMM) and decoding via Gaussian Mixture Regression
(GMR), the robot’s Cartesian impedance profile is estimated and replicated. We demonstrate the proposed method in a collaborative
sawing task with leader-follower structure, considering environmental constraints and dynamic uncertainties. The experimental
setup includes two Panda robots, which replicate the leader-follower roles and the impedance profiles extracted from a two-persons
sawing video.
- URI
- https://arxiv.org/abs/2204.04003https://repository.hanyang.ac.kr/handle/20.500.11754/170779
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ETC
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML