Conclusion

Meeting Design Criteria: In this project, we successfully enabled the Sawyer robot to identify objects that needed classification and precisely grasp the identified objects with its robotic arm, placing them into either recyclable or non-recyclable bins.

Difficulties Encountered: During the experiments, the success rate of grasping varied across different objects. The success rate for grasping paper and cloth reached 99%, while it was only 25% for cans and 50% for apples. These differences in success rates stem from the characteristics of the objects themselves. Due to recognition errors from the camera, there was a certain deviation in the selection of the center point for each grasp. Paper and cloth, with their large surface areas, had higher success rates during grasping. In contrast, cans had a lower success rate due to their smaller contact area and reduced friction, which made them more prone to slipping and other issues. 

Potential Improvements: In the future, we aim to further reduce errors by designing improved gripper mechanisms and updating the code, making intelligent grasping applicable to a wider range of objects and scenarios. 

Migrating the image classification process to a local GPU or edge device would eliminate communication delays, enhancing real-time responsiveness.

Increasing the diversity and volume of training examples would improve the model's classification accuracy and robustness against varied object appearances.