Abstract: Systems and methods are disclosed for image-based object detection and classification. For example, methods may include accessing an image from an image sensor; applying a convolutional neural network to the image to obtain localization data to detect an object depicted in the image and to obtain classification data to classify the object, in which the convolutional neural network has been trained in part using training images with associated localization labels and classification labels and has been trained in part using training images with associated classification labels that lack localization labels; annotating the image based on the localization data and the classification data to obtain an annotated image; and storing, displaying, or transmitting the annotated image.

Abstract: Methods and apparatus related to training and/or utilizing a convolutional neural network to generate grasping parameters for an object. The grasping parameters can be used by a robot control system to enable the robot control system to position a robot grasping end effector to grasp the object. The trained convolutional neural network provides a direct regression from image data to grasping parameters. For example, the convolutional neural network may be trained to enable generation of grasping parameters in a single regression through the convolutional neural network. In some implementations, the grasping parameters may define at least: a “reference point” for positioning the grasping end effector for the grasp; and an orientation of the grasping end effector for the grasp.

Abstract: Systems and methods are disclosed for image-based object detection and classification. For example, methods may include accessing an image from an image sensor; applying a convolutional neural network to the image to obtain localization data to detect an object depicted in the image and to obtain classification data to classify the object, in which the convolutional neural network has been trained in part using training images with associated localization labels and classification labels and has been trained in part using training images with associated classification labels that lack localization labels; annotating the image based on the localization data and the classification data to obtain an annotated image; and storing, displaying, or transmitting the annotated image.

Abstract: Systems and methods are disclosed for image-based object detection and classification. For example, methods may include accessing an image from an image sensor; applying a convolutional neural network to the image to obtain localization data to detect an object depicted in the image and to obtain classification data to classify the object, in which the convolutional neural network has been trained in part using training images with associated localization labels and classification labels and has been trained in part using training images with associated classification labels that lack localization labels; annotating the image based on the localization data and the classification data to obtain an annotated image; and storing, displaying, or transmitting the annotated image.

Abstract: Systems and methods are disclosed for image-based object detection and classification. For example, methods may include accessing an image from an image sensor; applying a convolutional neural network to the image to obtain localization data to detect an object depicted in the image and to obtain classification data to classify the object, in which the convolutional neural network has been trained in part using training images with associated localization labels and classification labels and has been trained in part using training images with associated classification labels that lack localization labels; annotating the image based on the localization data and the classification data to obtain an annotated image; and storing, displaying, or transmitting the annotated image.

Abstract: Methods and apparatus related to training and/or utilizing a convolutional neural network to generate grasping parameters for an object. The grasping parameters can be used by a robot control system to enable the robot control system to position a robot grasping end effector to grasp the object. The trained convolutional neural network provides a direct regression from image data to grasping parameters. For example, the convolutional neural network may be trained to enable generation of grasping parameters in a single regression through the convolutional neural network. In some implementations, the grasping parameters may define at least: a “reference point” for positioning the grasping end effector for the grasp; and an orientation of the grasping end effector for the grasp.