Grasp State Classification in Agricultural Manipulation

The agricultural setting poses additional challenges for robotic manipulation, as fruit is firmly attached to plants and the environment is cluttered and occluded. Therefore, accurate feedback about the grasp state is essential for effective harvesting. This study examines the different states involved in fruit picking by a robot, such as successful grasp, slip, and failed grasp, and develops a learning-based classifier using low-cost, computationally light sensors (IMU and IR reflectance). The Random Forest multi-class classifier accurately determines the current state and along with the sensors can operate in the occluded environment of a plant. The classifier was successfully trained and tested in the lab and showed 100% success at identifying slip and grasp failure and 80% success identifying successful picks on a real cherry tomato plant. By using this classifier, corrective actions can be planned based on the current state, thus leading to more efficient fruit harvesting.