System and method for unknown object manipulation from pure synthetic stereo data

Patent metadata

Field	Value
Publication number	US-2023398692-A1
Application number	US-202217839193-A
Country	US
Kind code	A1
Filing date	Jun 13, 2022
Priority date	Jun 13, 2022
Publication date	Dec 14, 2023
Grant date	—

Abstract

A method for training a neural network to perform 3D object manipulation is described. The method includes extracting features from each image of a synthetic stereo pair of images. The method also includes generating a low-resolution disparity image based on the features extracted from each image of the synthetic stereo pair of images. The method further includes generating, by the neural network, a feature map based on the low-resolution disparity image and one of the synthetic stereo pair of images. The method also includes manipulating an unknown object perceived from the feature map according to a perception prediction from a prediction head.

First claim

What is claimed is:

1 . A method for training a neural network to perform 3D object manipulation, the method comprising:
extracting features from each image of a synthetic stereo pair of images; generating a low-resolution disparity image based on the features extracted from each image of the synthetic stereo pair of images: generating, by the neural network, a feature map based on the low-resolution disparity image and one of the synthetic stereo pair of images; and manipulating an unknown object perceived from the feature map according to a perception prediction from a prediction head.

2 . The method of claim 1 , in which manipulating comprises:
generating, by the prediction head, oriented bounding box (OBB) predictions based on the feature map; producing grasp positions according to the OBB predictions; and grasping the unknown object based on the grasp positions.

3 . The method of claim 1 , further comprising:
generating non-photorealistic simulation graphics; and generating the synthetic stereo pair of images from the non-photorealistic simulation graphics to provide a left image and a right image as the synthetic stereo pair of images.

4 . The method of claim 1 , further comprising generating a segmentation image based on the feature map.

5 . The method of claim 1 , further comprising detecting keypoints of objects in the synthetic stereo pair of images detected from the feature map.

6 . The method of claim 1 , further comprising generating a full resolution disparity image from the synthetic stereo pair of images based on the feature map.

7 . The method of claim 1 , further comprising planning the manipulating of the unknown object by a robot according to the perception prediction from the prediction head.

8 . The method of claim 1 , further comprising planning an object grasp by a robot according to object grasp predictions from video captured by the robot.

9 . A non-transitory computer-readable medium having program code recorded thereon for training a neural network to perform 3D object manipulation, the program code being executed by a processor and comprising:
program code to extract features from each image of a synthetic stereo pair of images; program code to generate a low-resolution disparity image based on the features extracted from each image of the synthetic stereo pair of images: program code to generate a feature map based on the low-resolution disparity image and one of the synthetic stereo pair of images using the neural network; and program code to manipulate an unknown object perceived from the feature map according to a perception prediction from a prediction head.

10 . The non-transitory computer-readable medium of claim 9 , in which the program code to manipulate comprises:
program code to generate, by the prediction head, oriented bounding box (OBB) predictions based on the feature map; program code to produce grasp positions according to the OBB predictions; and program code to grasp the unknown object based on the grasp positions.

11 . The non-transitory computer-readable medium of claim 9 , further comprising:
program code to generate non-photorealistic simulation graphics; and program code to generate the synthetic stereo pair of images from the non-photorealistic simulation graphics to provide a left image and a right image as the synthetic stereo pair of images.

12 . The non-transitory computer-readable medium of claim 9 , further comprising program code to generate a segmentation image based on the feature map.

13 . The non-transitory computer-readable medium of claim 9 , further comprising program code to detect keypoints of objects in the synthetic stereo pair of images detected from the feature map.

14 . The non-transitory computer-readable medium of claim 9 , further comprising program code to generate a full resolution disparity image from the synthetic stereo pair of images based on the feature map.

15 . The non-transitory computer-readable medium of claim 9 , further comprising program code to plan the manipulating of the unknown object by a robot according to the perception prediction from the prediction head.

16 . The non-transitory computer-readable medium of claim 9 , further comprising program code to plan an object grasp by a robot according to object grasp predictions from video captured by the robot.

17 . A system for training a neural network to perform 3D object manipulation, the system comprising:
a stereo feature extraction module to extract features from each image of a synthetic stereo pair of images; a disparity image generation module to generate a low-resolution disparity image based on the features extracted from each image of the synthetic stereo pair of images: a feature map generation module to generate a feature map based on the low-resolution disparity image and one of the synthetic stereo pair of images using the neural network; and a 3D object manipulation module to manipulate an unknown object perceived from the feature map according to a perception prediction from a prediction head.

18 . The system of claim 17 , in which the 3D object manipulation module is further to generate, by the prediction head, oriented bounding box (OBB) predictions based on the feature map, to produce grasp positions according to the OBB predictions, and to grasp the unknown object based on the grasp positions.

19 . The system of claim 17 , further a planner module to plan the manipulating of the unknown object by a robot according to the perception prediction from the prediction head.

20 . The system of claim 17 , further a planner module to plan an object grasp by a robot according to object grasp predictions from video captured by the robot.

Assignees

• Toyota Res Inst Inc
• Toyota Motor Co Ltd

Inventors

• Kollar Thomas
• Stone Kevin
• Laskey Michael
• Tjersland Mark Edward

Classifications

• B25J9/1697 Primary

  Vision controlled systems · CPC title

• H04N13/128

  Adjusting depth or disparity · CPC title

• H04N13/275

  from three-dimensional [3D] object models, e.g. computer-generated stereoscopic image signals · CPC title

• G06T7/10

  Segmentation; Edge detection (motion-based segmentation G06T7/215) · CPC title

• G06T7/73

  using feature-based methods · CPC title