Custom CNN for Object State Classification in Robotic Cooking

This project presents the development of a custom Convolutional Neural Network (CNN) designed to classify object states—such as sliced, diced, or peeled—in cooking environments. Recognizing fine-grained object states is essential for context-aware manipulation but remains challenging due to visual similarity between states and a limited dataset. To address these challenges, I built a lightweight CNN from scratch, deliberately avoiding pretrained models to maintain domain specificity and efficiency. The model was enhanced through data augmentation and optimized dropout layers, with additional experiments incorporating batch normalization, Inception modules, and residual connections. While these advanced techniques offered incremental improvements during experimentation, the final model—a combination of data augmentation, dropout, and batch normalization—achieved ~60% validation accuracy and demonstrated stable generalization. This work highlights the trade-offs between model complexity and performance in constrained environments and contributes toward real-time state recognition with potential applications in assistive technologies.