Grasp modelling

Patent metadata

Field	Value
Publication number	US-10099369-B2
Application number	US-201414892797-A
Country	US
Kind code	B2
Filing date	May 21, 2014
Priority date	May 21, 2013
Publication date	Oct 16, 2018
Grant date	Oct 16, 2018

Abstract

A method of generating a configuration of a robotic hand for automatically grasping a first object, the robotic hand comprising a plurality of parts, is provided. The method comprises: receiving data representing the first object; receiving a plurality of first models generated based upon an example grasp of a second object, the example grasp being based upon a configuration of the robotic hand for grasping the second object in which a plurality of parts of said hand contact said second object, each of said plurality of first models representing a relationship between a respective part of the robotic hand and a property of the second object associated with said part of the robotic hand; and processing the data representing the first object based upon the plurality of first models to determine said configuration of the robotic hand for automatically grasping the first object.

First claim

The invention claimed is:

1. A method of generating a configuration of a robotic hand for automatically grasping a first object, the robotic hand comprising a plurality of parts, the method comprising:
receiving data representing the first object;
receiving a plurality of first models generated based upon an example grasp of a second object, the example grasp being based upon a configuration of the robotic hand for grasping the second object in which a plurality of parts of said hand contact said second object, each of said plurality of first models representing a relationship between a respective part of the robotic hand and a property of the second object associated with said part of the robotic hand, wherein the relationship is independent of any other part of the robotic hand;
receiving a second model generated based upon the example grasp, the second model representing a relationship between the plurality of parts of the robotic hand; and processing the data representing the first object based upon the plurality of first models to determine said configuration of the robotic hand for automatically grasping the first object.

2. A method according to claim 1 , wherein processing the data representing the object based upon the plurality of first models comprises: determining a relationship between each of said plurality of first models and said data representing the first object associated with a plurality of locations of said first object; wherein said configuration of the robotic hand for automatically grasping the first object is determined based upon the determined relationships for the plurality of first models.

3. A method according to claim 2 , wherein determining a relationship between each of the plurality of first models and the data representing the first object comprises, for each of said first models: determining correspondence between the property of the second object associated with the respective part of the robotic hand and a property of each of the plurality of locations of the first object; wherein said configuration of the robotic hand for automatically grasping the first object is determined based upon the determined correspondence.

4. A method according to claim 1 , wherein the property of the second object is a property generated based upon data representing the second object associated with a region of the second object that has a predetermined spatial relationship with the respective part of the robotic hand when the second object is grasped in the example grasp.

5. A method according to claim 4 , wherein the predetermined spatial relationship is based upon contact with the respective part of the robotic hand.

6. A method according to claim 1 , wherein each of the received plurality of first models represents a relationship between a spatial arrangement of the respective part of the robotic hand relative to the second object and the property of the second object associated with the part of the robotic hand.

7. A method according to claim 1 , wherein each of the received plurality of first models comprises a probability density function.

8. A method according to claim 1 , wherein the property is a property of a surface of the region of the object, wherein the property is based upon curvature of the surface of the region of the object.

9. A method according to claim 1 , wherein processing the data representing the first object comprises determining a relationship between at least one of said plurality of first models and a location associated with said first object based upon said second model.

10. A method according to claim 9 , wherein determining a relationship between at least one of said plurality of first models and a location associated with said first object based upon said second model comprises: determining a location associated with a selected one of said first models based upon a relationship between the selected one of said plurality of first models and said data representing the first object; determining a configuration of the robotic hand based upon the determined location associated with the selected one of the first models; and determining a location associated with the at least one of said plurality of first models based upon the determined configuration.

11. A method according to claim 10 , further comprising generating a score associated with said determined configuration of the robotic hand, said score being determined based upon a relationship between said at least one of said plurality of first models and said data representing the first object associated with said determined location.

12. A method according to claim 11 , further comprising: determining a location associated with each of said plurality of first models based upon the determined configuration; wherein said score is determined based upon a relationship between each of said plurality of first models and the associated locations.

13. A method according to claim 11 , further comprising generating a plurality of scores based upon a plurality of determined configurations of the robotic hand, each configuration of the robotic hand being based upon a determined location associated with a different selected one of the first models; wherein said configuration of the robotic hand for automatically grasping the first object is determined based upon said plurality of scores.

14. A method according to claim 1 , wherein the relationship between the plurality of parts of the robotic hand represented by the second model comprises a relationship between the plurality of parts of the robotic hand when the robotic hand is in contact with the second object during the example grasp.

15. A method according to claim 1 , wherein the relationship between the plurality of parts of the robotic hand represented by the second model comprises a relationship between the plurality of parts of the robotic hand prior to the robotic hand contacting the second object during the example grasp.

16. A method according to claim 1 , wherein the data representing the first object comprises image data.

17. A method according to claim 1 , wherein said received plurality of first models are generated based upon a plurality of example grasps of a second object, wherein each of said plurality of example grasps being associated with a respective second object.

18. A method according to claim 1 , wherein at least two of said plurality of first models represent a relationship between the same part of the robotic hand and a property of the second object associated with said part of the robotic hand and wherein said relationship between a respective part of the robotic hand and a property of the second object associated with said part of the robotic hand is based upon a volume defined relative to the part of the robotic hand, and wherein processing the data representing the first object based upon the plurality of first models comprises combining data associated with a plurality of first models using a product of mixtures approach, and wherein said plurality of models for which data is combined is generated using machine learning techniques.

19. A non-transitory computer program comprising computer readable instructions configured to cause a computer to carry out a method of generating a configuration of a robotic hand for automatically grasping a first object, the robotic hand comprising a plurality of parts, the method comprising:
receiving data representing the first object;
receiving a plurality of first models generated based upon an example grasp of a second object, the example grasp being

Assignees

• Univ Birmingham

Inventors

• Kopicki Marek

Classifications

• B25J9/1612 Primary

  characterised by the hand, wrist, grip control · CPC title

• B25J9/1697

  Vision controlled systems · CPC title

• G05B2219/39543

  Recognize object and plan hand shapes in grasping movements · CPC title

• Y10S901/36

  Actuating means · CPC title

• B25J9/163

  learning, adaptive, model based, rule based expert control · CPC title