Systems and methods for learning to extrapolate optimal object routing and handling parameters

US11407589B2 · kind B2 · utility

Assignee

• Berkshire Grey Operating Company, Inc. · US

Inventors

• Thomas Wagner · Concord, US
• Matthew T. Mason · Pittsburgh, US
• Thomas Koletschka · Cambridge, US
• Abraham D. Schneider · San Francisco, US
• Shervin Javdani · San Francisco, US
• Christopher Geyer · Arlington, US

Key dates

• Filing date: Oct 25, 2019
• Grant date: Aug 9, 2022
• Expiry date: Feb 12, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G05B2219/45063
• WIPO field: Handling
• WIPO sector: Mechanical engineering

Abstract

A system for object processing is disclosed. The system includes a framework of processes that enable reliable deployment of artificial intelligence-based policies in a warehouse setting to improve the speed, reliability, and accuracy of the system. The system harnesses a vast number of picks to provide data points to machine learning techniques. These machine learning techniques use the data to refine or reinforce in-use policies to optimize the speed and successful transfer of objects within the system. For example, objects in the system are identified at a supply location, a predetermined set of information regarding object is retrieved and combined with a set of object information and processing parameters determined by the system. The combined information is then used to determine routing of the object according to an initial policy. This policy is then observed, altered, tested, and re-implemented in an altered form.