The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 25, 2025

Filed:

Oct. 20, 2022
Applicant:

Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US);

Inventors:

Devesh Jha, Cambridge, MA (US);

Diego Romeres, Cambridge, MA (US);

Daniel Nikovski, Cambridge, MA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
B25J 9/16 (2006.01); B25J 13/08 (2006.01);
U.S. Cl.
CPC ...
B25J 9/163 (2013.01); B25J 9/1664 (2013.01); B25J 9/1671 (2013.01); B25J 13/085 (2013.01); G05B 2219/23246 (2013.01); G05B 2219/40395 (2013.01);
Abstract

A robotic controller is provided for generating sequences of movement primitives for sequential tasks of a robot having a manipulator. The controller includes at least one control processor, and a memory circuitry storing a dictionary including the movement primitives, a pretrained learning module, and a graph-search based planning module having instructions stored thereon. The controller to perform steps acquiring a planned task provided by an interface device operated by a user, wherein the planned task is represented by an initial state and a goal state with respect to an object, generating a planning graph by searching a feasible path of the object for the novel task using the graph-search based planning module and selecting movement primitives from the dictionary in the pretrained learning module, wherein the pretrained learning module has been trained based on demonstration tasks, parameterizing the feasible path represented by the movement primitives as dynamic movement primitives (DMPs) using the initial state and goal state, and implementing the parameterized feasible path as a trajectory according to the selected movement primitives using the manipulator of the robot by tracking and following the parameterized for the planned task.


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