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

TL;DR

This paper develops methods for understanding pick-and-place tasks in robotic scenes by analyzing initial and final images, introducing a dataset, and comparing geometric and CNN-based approaches for task detection.

Contribution

It introduces a new dataset and two novel methods—geometric and CNN-based—for detecting pick-and-place tasks from scene transformations.

Findings

CNN-based method achieves 84.3% success rate

CNN method outperforms geometric approach by ~12 percentage points

Object detection with YOLOv5 effectively supports task analysis

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%.