Xinyi Zhang

I'm a Ph.D. student at Harada Lab. in Osaka University, advised by Professor Kensuke Harada. My research focuses on perception and planning for robotic bin picking. I received my M.E. from Osaka University and B.E. from Tianjin University, China.

Publication / Resource / Github / YouTube

E-mail: chou [at]


Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Xinyi Zhang, Yukiyasu Domae, Weiwei Wan, Kensuke Harada

Paper / Video / Code

Wire harnesses are essential connecting components in manufacturing industry but are challenging to be automated in industrial tasks such as bin picking. They are long, flexible and tend to get entangled when randomly placed in a bin. This makes the robot struggle to pick a single one from the clutter. Besides, modeling wire harnesses is difficult due to the complex structures of combining deformable cables with rigid components, making it unsuitable for training or collecting data in simulation. In this work, instead of directly lifting wire harnesses, we proposed to grasp and extract the target following circle-like trajectories until it is separated from the clutter. We learn a policy from real-world data to infer the optimal action and grasp from visual observation. Our policy enables the robot to perform non-tangle pickings efficiently by maximizing success rates and reducing the execution time. To evaluate our policy, we present a set of real-world experiments on picking wire harnesses. Results show a significant improvement in success rates from 49.2% to 84.6% over the tangle-agnostic bin picking method. We also evaluate the effectiveness of our policy under different clutter scenarios using unseen types of wire harnesses. The proposed method is expected to provide a practical solution for automating manufacturing processes with wire harnesses.

A Topological Solution of Entanglement for Complex-shaped Parts in Robotic Bin-picking

Xinyi Zhang, Keisuke Koyama, Yukiyasu Domae, Weiwei Wan, Kensuke Harada

IEEE International Conference on Automation Science and Engineering (CASE 2021)

Paper / Video / Code

This paper addresses the problm of picking up only one object at a time avoiding any entanglement in bin-picking. To cope with a difficult case where the complex-shaped objects are heavily entangled together, we propose a topology-based method that can generate non-tangle grasp positions on a single depth image. The core technique is the entanglement map, which is a feature map to measure the entanglement possibilities obtained from the input image. We use an entanglement map to select probable regions containing graspable objects. The optimum grasping pose is detected from the selected regions considering the collision between robot hand and objects. Experimental results show that our analytic method provides a more comprehensive and intuitive observation of the entanglement and exceeds previous learning-based work in success rates. Especially, our topology-based method does not rely on any object models or time-consuming training process, so that it can be easily adapted to more complex bin-picking scenes.


B.S. in Information Management and Information System, Tianjin University, China

September, 2012 - July, 2016

Research student in Systems Science and Applied Informatics, Osaka University, Japan

October, 2016 - March, 2018

M.S. in Systems Science and Applied Informatics, Osaka University, Japan

April, 2018 - March, 2020

Ph.D. student in Systems Science and Applied Informatics, Osaka University, Japan

April, 2020 -


Harada Lab.

Division of Systems Science and Applied Informatics

Graduate School of Engineering Science

Osaka University