Online self-learning method for robot autonomous object picking task

Abstract

The invention provides an online self-learning method for robot autonomous object picking based on area prediction. The robot stereo camera obtains the RGBD information of the stacked objects to be grasped, divides the RGB image and the depth map into blocks at a certain step distance, and pairs the above image blocks into the area prediction neural network module to obtain the absorbability probability area map. The figure selects the grabbing point in the image coordinate system and converts it to the robot operation coordinate system. The robot executes the grabbing point, and finally trains the area prediction network online according to the robot execution result. This method uses the deep neural network to directly obtain the grasping point, avoiding the object segmentation and pose estimation problems usually faced by object picking in complex backgrounds, and at the same time using the execution results of the robot to train the prediction network online, effectively realizing the object picking The online self-learning of the robot avoids complex feature selection or manual labeling of samples and neural network training problems.

Description

technical field [0001] The invention relates to the field of self-learning of robots, in particular to an online self-learning method for autonomous object picking tasks of robots. In particular, it relates to an online self-learning method and system for robot autonomous object grasping involving scattered and piled objects. Background technique [0002] The robot's autonomous learning ability weakens the detailed setting requirements for specific tasks in robot applications and improves the robot's adaptability to different scenarios. It is also an important feature of robot intelligence and has a wide range of application values. There is a wide range of application requirements for the efficient picking of messy and stacked objects in unstructured scenarios, such as automatic sorting of garbage in garbage disposal scenarios, automatic sorting of logistics packages, and automatic loading and unloading in industrial application scenarios. Traditional object picking method...

AI Technical Summary

Problems solved by technology

Traditional object picking methods mostly use a series of processes such as object recognition, object segmentation, and pose estimation to determine the grabbing point to achieve object grabbing. However, stacked objects have complex environmental backgrounds and mutual positional relationships. challenge

At the same time, the variability of application scenarios also brings a huge workload to the specific detailed settings for each scenario.

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