Plus One Robotics, Inc.

Daniel Grollman

Shaun Edwards

Maulesh Trivedi

The present disclosure is for systems and methods for adjusting operational configurations of robots in real-time. The invention pertains to overriding or replacing one operational configuration of a robot with another when appropriate circumstances arise and certain conditions have been met. In one aspect, the invention is applicable to robotic picking operations and serves to allow for unique robotic picking operations outside of the normal or standard limitations typically imposed on a robotic picking system. The invention provides the ability to remotely adjust robotic operational configurations in real-time, on-demand, in order to address various circumstances that may arise without requiring interruption of a picking session or requiring on-site human intervention.

Systems and methods for determining operational paradigms for robotic picking based on pick data source

The present invention relates to systems and methods for accounting for edge cases (i.e. tail data) in automated decision making systems, for example automated robotic picking systems. The systems and methods provide for retraining machine learning (ML) models so that the edge cases can be handled in a manner that requires less (or no) human intervention. The disclosed systems and methods create updated ML models, replacement ML models, and/or supplementary ML models that can provide better performance (e.g. improved automated robotic picking) when edge cases are encountered. Furthermore, the present inventions disclose systems and methods for obtaining training data faster and in a more cost effective manner, which enables the systems and methods disclosed herein to update models at a faster rate, thereby enabling broader, system-wide handling of edge cases in a more effective and efficient manner.

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Boulder, CO, United States of America

Daniel Grollman