2021/06/29
ID: 3766

Better gripping with intelligent picking robots

Researchers from Germany and Canada work on new AI methods for picking robots

Production, warehouse, shipping - where goods are produced, stored, sorted or packed, picking also takes place. This means that several individual goods are removed from storage units such as boxes or cartons and reassembled. With the FLAIROP (Federated Learning for Robot Picking) project Festo and researchers from the Karlsruhe Institute of Technology (KIT), together with partners from Canada, want to make picking robots smarter using distributed AI methods. To do this, they are investigating how to use training data from multiple stations, from multiple plants, or even companies without requiring participants to hand over sensitive company data. 

We are investigating how the most versatile training data possible from multiple locations can be used to develop more robust and efficient solutions using artificial intelligence algorithms than with data from just one robot," says Jonathan Auberle from the Institute of Material Handling and Logistics (IFL) at KIT. In the process, items are further processed by autonomous robots at several picking stations by means of gripping and transferring. At the various stations, the robots are trained with very different articles. At the end, they should be able to grasp articles from other stations that they have not yet learned about. "Through the approach of federated learning, we balance data diversity and data security in an industrial environment," says the expert.

Powerful algorithms for industry and logistics 4.0

Until now, federated learning has been used predominantly in the medical sector for image analysis, where the protection of patient data is a particularly high priority. Consequently, there is no exchange of training data such as images or grasp points for training the artificial neural network. Only pieces of stored knowledge - the local weights of the neural network that tell how strongly one neuron is connected to another - are transferred to a central server. There, the weights from all stations are collected and optimized using various criteria. Then the improved version is played back to the local stations and the process repeats. The goal is to develop new, more powerful algorithms for the robust use of artificial intelligence for industry and Logistics 4.0 while complying with data protection guidelines.

In the FLAIROP research project, we are developing new ways for robots to learn from each other without sharing sensitive data and company secrets. This brings two major benefits: we protect our customers' data and we gain speed because the robots can take over many tasks more quickly. In this way, the collaborative robots can, for example, support production workers with repetitive, heavy, and tiring tasks”, explains Jan Seyler, Head of Advanced Develop. Analytics and Control at Festo SE & Co. KG

During the project, a total of four autonomous picking stations will be set up for training the robots: Two at the KIT Institute for Material Handling and Logistics (IFL) and two at the Festo SE company based in Esslingen am Neckar.

Start-up DarwinAI and University of Waterloo from Canada are further partners

“DarwinAI is thrilled to provide our Explainable (XAI) platform to the FLAIROP project and pleased to work with such esteemed Canadian and German academic organizations and our industry partner, Festo. We hope that our XAI technology will enable high-value human-in-the-loop processes for this exciting project, which represents an important facet of our offering alongside our novel approach to Federated Learning.  Having our roots in academic research, we are enthusiastic about this collaboration and the industrial benefits of our new approach for a range of manufacturing customers”, says Sheldon Fernandez, CEO, DarwinAI.

费斯托 (Festo)是一家全球性的独立的家族企业,总部位于德国埃斯林根。自成立以来,Festo在工业自动化技术和技术教育方面制定标准,从而为环境、经济和社会的可持续发展做出贡献。公司为超过35个行业的30万家工厂和过程自动化客户提供气动和电驱动自动化技术解决方案,其中生命科学和实验室自动化业务受到越来越多的关注。Festo产品和服务遍布176个国家。2021年,费斯托在全球61个国家的250多个分支机构拥有约20700名员工,实现销售额33.6亿欧元。每年约7%的销售额用于研发。在这家学习型企业,1.5%的销售额用于基础和进一步培训。Festo 教学培训 (Didactic SE) 是全球领先的技术教育和培训供应商,为全球客户提供工业环境中全面的数字化和常规学习解决方案。

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FLAIROP
In the research project FLAIROP, the robots are trained with different articles in separate locations. The weights from all stations are collected and optimized using various criteria. Then the improved version is played back to the local stations...