Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes

TL;DR

This work focuses on scene understanding to detect pick-and-place tasks given initial and final images from the scene and proposes two methods to detect the pick-and-place tasks which transform the initial scene into the final scene.

Abstract

With robots increasingly collaborating with humans in everyday tasks, it is important to take steps toward robotic systems capable of understanding the environment. This work focuses on scene understanding to detect pick and place tasks given initial and final images from the scene. To this end, a dataset is collected for object detection and pick and place task detection. A YOLOv5 network is subsequently trained to detect the objects in the initial and final scenes. Given the detected objects and their bounding boxes, two methods are proposed to detect the pick and place tasks which transform the initial scene into the final scene. A geometric method is proposed which tracks objects' movements in the two scenes and works based on the intersection of the bounding boxes which moved within scenes. Contrarily, the CNN-based method utilizes a Convolutional Neural Network to classify objects with intersected bounding boxes into 5 classes, showing the spatial relationship between the involved objects. The performed pick and place tasks are then derived from analyzing the experiments with both scenes. Results show that the CNN-based method, using a VGG16 backbone, outscores the geometric method by roughly 12 percentage points in certain scenarios, with an overall success rate of 84.3%.

Figures (5)

• Figure 1: The pick-and-place setup used in this study and its components.
• Figure 2: Overall flow of the proposed methods. Both geometric and CNN-based methods use object detection results. The geometric method tracks the bounding boxes in the initial and final images, while the CNN-based method uses a CNN network to detect spatial relationships between objects whose bounding boxes overlap and identify the pick-and-place operations that transform the initial image into the final image.
• Figure 3: Dataset collection procedure. In (a) a set of objects and pick-and-place tasks are presented by the GUI, (b) shows the initial scene image set up by the user, and (c) shows the scene after the performed pick-and-place tasks.
• Figure 4: Confusion matrix of classification results over validation dataset for (a) ResNet-50, (b) ResNet-101, and (c) VGG16.
• Figure 5: Three unseen initial and final scenes, object detection results and confidence score, and predicted pick-and-place tasks by geometric method displayed with (green arrow ), CNN-based methods displayed with (purple arrow), and the ground truth displayed with (black arrow).