Classifying cooking object's state using a tuned VGG convolutional neural network
This work addresses the need for robots to recognize object states for grasping and manipulation in cooking, but it is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of classifying cooking object states (e.g., diced, julienne) using a tuned VGG-16 convolutional neural network, achieving 77% accuracy on a dataset created for this purpose.
In robotics, knowing the object states and recognizing the desired states are very important. Objects at different states would require different grasping. To achieve different states, different manipulations would be required, as well as different grasping. To analyze the objects at different states, a dataset of cooking objects was created. Cooking consists of various cutting techniques needed for different dishes (e.g. diced, julienne etc.). Identifying each of this state of cooking objects by the human can be difficult sometimes too. In this paper, we have analyzed seven different cooking object states by tuning a convolutional neural network (CNN). For this task, images were downloaded and annotated by students and they are divided into training and a completely different test set. By tuning the vgg-16 CNN 77% accuracy was obtained. The work presented in this paper focuses on classification between various object states rather than task recognition or recipe prediction. This framework can be easily adapted in any other object state classification activity.