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.
Furthermore, presented an interesting dataset of daily interactive manipulations focused on the position, orientation, torque and force of manipulated objects @cite_20 . The proposed dataset could give us valuable insight on the different parameters needed to manipulate certain objects, and could help to design robots capable of adapting their way of grasping objects based on the motions to be executed. Some examples of cooking-related motions included in the dataset are stirring, pouring, mixing, and slicing.
@cite_20
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Robots that succeed in factories may struggle to complete even the simplest daily task that humans take for granted, because the change of environment makes the task exceedingly difficult. Aiming to teach robots to perform daily interactive manipulation in a changing environment using human demonstrations, we collected our own data of interactive manipulation. The dataset focuses on the position, orientation, force, and torque of objects manipulated in daily tasks. The dataset includes 1,603 trials of 32 types of daily motions and 1,596 trials of pouring alone, as well as helper code. We present our dataset to facilitate the research on task-oriented interactive manipulation.