Robotic device control optimization using spring lattice deformation model
AssigneeAmazon Technologies, Inc.
InventorsAndrew D. Marchese, William Clay Flannigan, Parris S. Wellman
Life of this patent
Estimating the term…
Abstract
Method and apparatus for training a machine learning model for controlling a robotic picking device having a suction device end effector. A robotic control operation and a candidate contact point for holding a first item using a suction device of a robotic picking arm are determined, by processing information describing the first item as an input to a machine learning model. A seal quality metric is estimated for the candidate contact point, based on a predicted deformed n-dimensional shape of the suction device and a n-dimensional shape associated with the first item. One or more weights within the machine learning model are refined based on the estimated seal quality metric.
Claims20
A computer-implemented method performed via execution of one or more computer processors, the computer-implemented method, comprising:
- determining, by at least one of the one or more computer processors processing a first set of inputs using a machine learning model for determining behavior of a robotic arm, a first behavior for retrieving an item from within a storage container by holding the item at a contact point using a suction device of the robotic arm;
- estimating, by at least one of the one or more computer processors, a likelihood of successfully retrieving the item using the determined first behavior, by determining an expected seal quality metric for the contact point, by processing an n-dimensional surface model of the item and physical properties of the suction device of the robotic arm, wherein determining the expected seal quality metric comprises:
- modelling deformation and contact between the suction device and the n-dimensional surface model at the contact point on the item, using at least one n-dimensional spring-lattice to model deformation and contact between the suction device and the n-dimensional surface model at the contact point, such that potential energy across the n-dimensional spring-lattice is minimized;
- and
- calculating the expected seal quality metric, using the n-dimensional spring-lattice and a fluids model relating fluid force and leak pathways;
- and
- training, by at least one of the one or more computer processors, the machine learning model to optimize the behavior of the robotic arm, based on the expected seal quality metric.
A system, comprising:
- one or more computer processors;
- and
- computer program code that, when executed by operation of the one or more computer processors, performs an operation comprising:
- determining a robotic control operation and a candidate contact point for holding a first item using a suction device of a robotic picking arm, by processing information describing the first item as an input to a machine learning model;
- estimating a seal quality metric for the candidate contact point, based on a predicted deformed n-dimensional shape of the suction device and a n-dimensional shape associated with the first item;
- and
- refining one or more weights within the machine learning model based on the estimated seal quality metric.
A non-transitory computer-readable medium containing computer program code that, when executed by operation of one or more computer processors, performs an operation comprising:
- dynamically generating a plurality of synthetic machine learning model input data using a synthetic input generation engine;
- determining, by processing the plurality of synthetic machine learning model input data as inputs to a machine learning model for determining control behavior for a robotic arm, a plurality of control behaviors for retrieving items by holding each item at a determined contact point using a suction device of the robotic arm;
- evaluating the plurality of control behaviors for the robotic arm, by estimating a seal quality metric for each control behavior, based on a predicted deformed n-dimensional shape of the suction device and a n-dimensional shape associated with an item;
- and
- refining the machine learning model, based on the estimated seal quality metrics.
Dependencies are inferred from the claim text.