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Rapid robotic imitation learning of force-torque tasks

A robot and robot system technology, applied in the direction of robots, instruments, manipulators, etc., can solve problems such as unsatisfactory static calculation workload

Active Publication Date: 2015-11-25
GM GLOBAL TECH OPERATIONS LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, existing task-execution-based training methods are not ideal in terms of the number of required training examples and the overall static computational effort

Method used

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  • Rapid robotic imitation learning of force-torque tasks
  • Rapid robotic imitation learning of force-torque tasks
  • Rapid robotic imitation learning of force-torque tasks

Examples

Experimental program
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Embodiment Construction

[0024] Referring to the drawings, in which like reference numerals designate like or similar parts throughout the several views, in figure 1 An exemplary robotic system 10 is shown in FIG. The robotic system 10 includes a robot 12 and a controller (C) 20 programmed to perform the method 100 and thereby train the robot 12 to perform a force-torque task via human-assisted task demonstration. Robot 12 may include a torso 15 , an arm 16 having an end effector 21 , and possibly, in an exemplary anthropomorphic embodiment, a head 19 . End effector 21 may be configured as any suitable device for performing the exemplary task, such as a grasper or an anthropomorphic hand with fingers 22 attached to wrist 24 or other arm segment 17, as shown. Fingers 22 in such embodiments may be motorized fingers, extensions, or other grippers.

[0025] A non-limiting exemplary force-torque work task (used below for simplicity and consistency) is the grasping of an object 18 in the form of a light ...

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Abstract

A method of training a robot to autonomously execute a robotic task includes moving an end effector through multiple states of a predetermined robotic task to demonstrate the task to the robot in a set of n training demonstrations. The method includes measuring training data, including at least the linear force and the torque via a force-torque sensor while moving the end effector through the multiple states. Key features are extracted from the training data, which is segmented into a time sequence of control primitives. Transitions between adjacent segments of the time sequence are identified. During autonomous execution of the same task, a controller detects the transitions and automatically switches between control modes. A robotic system includes a robot, force-torque sensor, and a controller programmed to execute the method.

Description

technical field [0001] The present invention relates to fast robotic imitation learning for force-torque tasks. Background technique [0002] Robots are electromechanical devices that can manipulate objects using a series of robotic links. Robotic linkages are interconnected by joints, each of which can be driven independently or interdependently by one or more actuators. Each robot joint represents an independent control variable or degree of freedom. An end effector is an end linkage that directly performs a task such as grasping a work tool or stacking parts. Typically, the robot is controlled to desired target values ​​via closed-loop force-, impedance- or position-based control laws. [0003] In manufacturing, there is a need for more flexible manufacturing and processing that can produce new or more varied products with a minimum amount of downtime. To accomplish this goal, the robot platform should be able to quickly adapt itself to new tasks without reprogramming...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B25J9/16B25J19/00
CPCB25J9/1664G05B19/423G05B2219/36442Y10S901/03B25J9/163
Inventor J.W.威尔斯D.W.佩顿R.M.尤伦布洛克L.库
Owner GM GLOBAL TECH OPERATIONS LLC
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