Amazon Technologies, Inc.

Ralf Herbrich

Barry James O'Brien

James Christopher Curlander

Marshall Friend Tappen

Michael Donoser

Pradeep Krishna Yarlagadda

Cédric Philippe Charles Jean Ghislain Archambeau

Leo Parker Dirac

Michael Brueckner

Described are techniques for storing and retrieving items using a robotic device for moving items. Any combinations of image data depicting a manipulator interacting with an item, sensor data from sensors instrumenting the manipulator or item, item data regarding characteristics of the item, and constraint data relating to characteristics of the robotic device may be used to generate one or more configurations for the robotic device. The configurations may include points of contact and force vectors for contacting the item using the robotic device.

System for configuring a robotic device for moving items

Described are techniques for storing and retrieving items using a robotic manipulator. Images depicting a human interacting with an item, sensor data from sensors instrumenting the human or item, data regarding physical characteristics of the item, and constraint data relating to the robotic manipulator or the item may be used to generate one or more configurations for the robotic manipulator. The configurations may include points of contact and force vectors for contacting the item using the robotic manipulator.

System for configuring a robotic manipulator

Variables of observation records to be used to generate a machine learning model are identified as candidates for quantile binning transformations. In accordance with a particular concurrent binning plan generated for a particular variable, a plurality of quantile binning transformations are applied to the particular variable, including a first transformation with a first bin count and a second transformation with a different bin count. The first and second transformations result in the inclusion of respective parameters or weights for binned features in a parameter vector of the model. In a post-training phase run of the model, at least one parameter corresponding to a binned feature is used to generate a prediction.

Concurrent binning of machine learning data

Described are techniques for storing and retrieving items using a robotic manipulator. Images depicting a human interacting with an item, sensor data from sensors instrumenting the human or item, data regarding physical characteristics of the item, and constraint data relating to the robotic manipulator may be used to generate one or more configurations for the robotic manipulator. Points of contact and force vectors of the configurations may correspond to the points of contact and force vectors determined from the images and sensor data.

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Falkensee, Germany

Ralf Herbrich