Cooking Object's State Identification Without Using Pretrained Model
This addresses the need for state recognition in robotic cooking, which is crucial for recipe execution and grasping, but the approach is incremental as it applies a standard CNN to a new domain.
The paper tackled the problem of identifying cooking object states for robotic cooking without using pretrained models, achieving 65.8% accuracy on an unseen test dataset.
Recently, Robotic Cooking has been a very promising field. To execute a recipe, a robot has to recognize different objects and their states. Contrary to object recognition, state identification has not been explored that much. But it is very important because different recipe might require different state of an object. Moreover, robotic grasping depends on the state. Pretrained model usually perform very well in this type of tests. Our challenge was to handle this problem without using any pretrained model. In this paper, we have proposed a CNN and trained it from scratch. The model is trained and tested on the dataset from cooking state recognition challenge. We have also evaluated the performance of our network from various perspective. Our model achieves 65.8% accuracy on the unseen test dataset.