GENERATING A MODEL FOR AN OBJECT ENCOUNTERED BY A ROBOT
Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.
1 . A method implemented by one or more processors, the method comprising:
identifying a representation of a three-dimensional (3D) object;
generating a plurality of rendered images based on the representation of the 3D object, wherein generating the rendered images based on the representation of the 3D object comprises:
rendering, using the representation of the 3D object, a first image that renders the 3D object and that includes first additional content; and
rendering, using the representation of the 3D object, a second image that renders the 3D object and that includes second additional content that is distinct from the first additional content;
generating training examples that each include a corresponding one of the rendered images as training example input and that each include training example output that is based on a feature of the 3D object in the corresponding one of the rendered images; and
providing the training examples for training of a machine learning model.
2 . The method of claim 1 , further comprising:
generating a first scene using the representation of the 3D object,
generating a second scene using the representation of the 3D object;
wherein rendering the first image with the first additional content comprises rendering the first image using the first scene; and
wherein rendering the second image with the second additional content comprises rendering the second image using the second scene.
3 . The method of claim 1 , wherein rendering the first image with the first additional content comprises including the first additional content, in rendering the first image, based on an environment of the robot.
4 . The method of claim 3 , wherein rendering the second image with the second additional content comprises including the second additional content, in rendering the second image, based on the environment of the robot.
5 . The method of claim 1 , wherein the training example output of each of the training examples includes a corresponding pose of the object in the corresponding one of the rendered images.
6 . The method of claim 1 , wherein the rendered images each include a plurality of color channels and a depth channel.
7 . The method of claim 1 , wherein the representation of the 3D object includes a trained machine learning model.
8 . The method of claim 1 , wherein rendering the first image with the first additional content comprises rendering the 3D object onto a first background, and wherein rendering the second image with second additional content comprises rendering the 3D object onto a second background that is distinct from the first background.
9 . The method of claim 6 , further comprising:
selecting the first background based on the environment of the robot.
10 . The method of claim 9 , further comprising:
selecting the second background based on the environment of the robot.
11 . The method of claim 1 , further comprising:
training the machine learning model using the training examples.
12 . A system comprising:
memory storing instructions;
one or more processors operable to execute the instructions to:
identify a representation of a three-dimensional (3D) object;
generate a plurality of rendered images based on the representation of the 3D object, wherein in generating the rendered images based on the representation of the 3D object one or more of the processors are to:
render, using the representation of the 3D object, a first image that renders the 3D object and that includes first additional content; and
render, using the representation of the 3D object, a second image that renders the 3D object and that includes second additional content that is distinct from the first additional content;
generate training examples that each include a corresponding one of the rendered images as training example input and that each include training example output that is based on a feature of the 3D object in the corresponding one of the rendered images; and
provide the training examples for training of a machine learning model.
13 . The system of claim 12 , wherein one or more of the processors are further operable to execute the instructions to:
generate a first scene using the representation of the 3D object,
generate a second scene using the representation of the 3D object;
wherein in rendering the first image with the first additional content one or more of the processors are to render the first image using the first scene; and
wherein in rendering the second image with the second additional content one or more of the processors are to render the second image using the second scene.
14 . The system of claim 12 , wherein in rendering the first image with the first additional content one or more of the processors are to include the first additional content, in rendering the first image, based on an environment of the robot.
15 . The system of claim 12 , wherein the training example output of each of the training examples includes a corresponding pose of the object in the corresponding one of the rendered images.
16 . The system of claim 12 , wherein the rendered images each include a plurality of color channels and a depth channel.
17 . The system of claim 12 , wherein the representation of the 3D object includes a trained machine learning model.
18 . The system of claim 12 , wherein in rendering the first image with the first additional content one or more of the processors are to render the 3D object onto a first background, and wherein in rendering the second image with second additional content one or more of the processors are to render the 3D object onto a second background that is distinct from the first background.
19 . The system of claim 18 , wherein one or more of the processors are further operable to execute the instructions to:
select the first background based on the environment of the robot.
20 . The system of claim 18 , wherein one or more of the processors are further operable to execute the instructions to:
train the machine learning model using the training examples.