Manufacturing technologies

Robot Motion Planning, Imitation, Reinforcement Learning

Industrial robots, Collaborative robots, System integration
９．応用分野＜日本語＞

Generating skillful robotic manipulation motion by humandemonstration

産業ロボット、協同ロボット、システムインテグレーション＜英語＞

Industrial Robots, Collaborative Robots, System Integration

Department of Systems Innovation, Graduate School of Engineering Science

１0．特許

Associate Professor Weiwei Wan

Researchmap https://researchmap.jp/weiweiwan

Professor Kensuke Harada

Researchmap https://researchmap.jp/kharadaなし

１1．上記ご研究に関連する画像（図表・イラスト・写真）

Abstract
There is a wide range of skillful motions in human manipulationtasks. It is challenging to robotize these motions using conventionalmotion planning and imitation methods. Under this background, wedeveloped a method to overcome the difficulties by distinguishing
between motion planning and the reuse of human teaching trajectories. In the method, we track the motion trajectories of an object
using visual recognition and extract the key pose or key pose sequences by differentiating the taught trajectories. Then, the trajectories for planning and imitation are separated and will be used formotion planning and imitation respectively to generate skillful motions. Experiments and analysis showed the efficacy of the method.

※ A4 版では 2～3 点（主要図表＋補足図表），A5 版では 1～2 点（主要図for linear motions but is difficult to extend to delicate and complexの貼り付けを予定しておりますので，ご提供をお願い致します。
ones. Our implementation
can solve the problem of delicate andcomplex※motions.
It can be combined with direct teaching to sim必ず主要図表のご指定をお願い致します。
plify robotic system integration. Our method is expected to promote※ 画像は印刷可能な解像度のものを電子ファイルにてご提供ください。
the RaaS industry, where robots and humans will be employedの関係で問題が生じた場合は，こちらでトリミング等の加工をさせてequally.
あります。

Manufacturing technologies▍ Background & Results
Recently, several examples of the introduction of robots in Smalland Medium-sized Enterprises (SMEs) have been presented. Robots have taken the place of humans to perform production tasks,
leading to an efficient and low-cost production environment. However, the introduction of robots requires coordination by experts,
which is not suitable for engineers of SMEs who do not have expertise and know-how in robotics. It has hindered the broad deployment of robots.
Under this background, robotizing human production tasks
through intuitive human instruction, a.k.a. intuitive teaching, ishighly demanded. Intuitive teaching has the potential to increase
productivity by facilitating the introduction of industrial robots andhas long been the focus of robotic integration. Currently, a promising method to achieve intuitive implementation is teaching with AI.
While there is progress in the development of AI techniques for extracting human behavior with deep learning, mimicking behavioral
primitives, and probabilistically generating collision-free behavior
with motion planning, applying them to complex behavior with skill
is very difficult. From the viewpoint of motion planning, complex
actions with skillfulness are a narrow path problem in motion planning. It isn't easy to solve them in a general way using conventional
methods.
This study extracts key poses or key pose sequences of skillful actions by using the derivative of human teaching trajectories
recognized by the vision and separate trajectories for planningand imitation. Then, by applying motion planning and reuse of thehuman teaching trajectory to each separated trajectory, we implemented intuitive teaching of tasks such as replacing a door or connecting a rotary connector. We also used reinforcement learning toextend the planned trajectory to similar parts (those that changesize or have multiple of the same geometric features). For more detailed information, please refer to related papers 1 and 2.

教示環境（細く、A4の場合図 Interface
1 として）
Demonstration

追跡した対象物の軌跡（主要）An Exampleof Human Demonstration and Object Trajectory Tracking▍ Significance of the research and Future perspective
The direct teaching method of cooperative robots is best suitedMotion Generated Using Considering Key Poses of the Demonstrated Trajectories

P a t e n tWang, Yan; Harada, Kensuke; Wan, Weiwei. Motion planning of skillful motions in assembly process through human demonstration. Advanced Robotics.
提案手法で生成したロボットの作業動作（主要）2020; 34(16): 1079-1093. doi: 10.1080/01691864.2020.1782260.
Wang, Yan; Beltran-Hernandez, Cristian C; Wan, Weiwei; Harada, Kensuke. Hybrid Trajectory and Force Learning of Complex Assembly Tasks: A Combined Learning Framework. IEEE Access. 2021; 9: 60175-60186. doi: 10.1109/ACCESS.2021.3073711.
１2. ご自身の顔写真のご提供
https://www.youtube.com/watch?v=xHLChh4Y2l0
URL
https://www.youtube.com/watch?v=B75rBqCLYRQ
K e y w o r d robot motion planning, imitation, reinforcement learning
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