Tuned Inception V3 for Recognizing States of Cooking Ingredients
This addresses the challenge of ingredient state detection for kitchen robots, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of recognizing states of cooking ingredients for robotics by fine-tuning the Inception V3 model, achieving validation on a dataset with eleven states.
Cooking is a task that must be performed in a daily basis, and thus it is an activity that many people take for granted. For humans preparing a meal comes naturally, but for robots even preparing a simple sandwich results in an extremely difficult task. In robotics, designing kitchen robots is complicated since cooking relies on a variety of physical interactions that are dependent on different conditions such as changes in the environment, proper execution of sequential instructions, along with motions, and detection of the different states in which cooking-ingredients can be in for their correct grasping and manipulation. In this paper, we focus on the challenge of state recognition and propose a fine tuned convolutional neural network that makes use of transfer learning by reusing the Inception V3 pre-trained model. The model is trained and validated on a cooking dataset consisting of eleven states (e.g. peeled, diced, whole, etc.). The work presented on this paper could provide insight into finding a potential solution to the problem.