Abb Schweiz Ag

Eth Zurich

Hitachi Energy Ltd

Planted Foods Ag

Sandro Schoenborn

Robert Birke

Thomas R Locher

Erich J Windhab

Yvonne-Anne Pignolet

Thanikesavan Sivanthi

Carsten Franke

Mehmet Mercangoez

Manuel Oriol

Vedrana Spudic

Petri Hovila

Maciej Orman

Theo Widmer

Ettore Ferranti

Chau-Hon Ho

Philipp Sommer

Raphael Eidenbenz

Eric Stirnemann

Elsi-Mari Borrelli

Kim Listmann

Patrick Rühs

Robert René Maria Birke

Andrei Dan

Lukas Böni

Lukas Boni

Patrick Ruhs

A method and system are provided for training a robot to recognize objects in the workspace of the robot. Objects in the workspace are identified by the user, and the robot determines candidate objects. Feedback may be used in order for the user to confirm whether the candidate object determined by the robot system matches the object intended by the user. Gripping information for the object may also be identified by the user to train the robot how to grip the object.

Method and system for training a robot

To facilitate storage of data from plural data sources of an industrial automation control system, power distribution system or power generation system, a decision making device executes a machine learning algorithm to determine a compression technique in dependence on the data source from which data originates.

Method and device for facilitating storage of data from an industrial automation control system or power system

A system includes a wet extrusion process machine configured to receive, mix, and convey a plurality of ingredients to an extrusion die, the plurality of ingredients include a protein powder, an oil, and water. The system includes an electronic process control system (EPCS) configured to control the wet extrusion machine using a plurality of process settings effective to produce an extrusion die mixture which is forced into, passes through, and is output from the extrusion die. The system further includes a supervisory machine intelligence control system (SMICS) operatively coupled with at least one of a direct fibrosity measurement (DFM) subsystem configured to directly measure one or more physical fibrosity parameters of the extrusion die mixture, and an indirect fibrosity measurement (IFM) subsystem configured to measure one or more extrusion process parameters associated with the extrusion die mixture. The SMICS is configured to modify one or more of the plurality process settings in response to at least one of the one or more physical fibrosity parameters, and the one or more extrusion process parameters, effective to modify the extrusion die mixture.

Supervisory machine intelligence controls for production of meat substitutes

Growing community of inventors

Basel, Switzerland

Sandro Schoenborn