Abstract: A system includes an engagement tool (100) having a suction device (224), a movement system (166) configured to move the engagement tool (100), an imaging system (316) configured to capture an image of a surface of a meat product (202), and a computing device. The computing device is configured to (i) determine, from the image of the surface of the meat product (202), a target region (2061) on the surface of the meat product (202) based on an estimated fat content in the meat product (202) at the target region (2061), (ii) cause the movement system to move the engagement tool (100) with respect to the meat product (202) such that the suction device (224) engages the target region (2061), and (iii) initiate a vacuum within the suction device (224) such that the engagement tool (100) exerts a force on the target region of the surface of the meat product (202) at the suction device (224).

Abstract: A gripping tool includes a vacuum manifold, a flexible membrane, and vacuum channels coupled in parallel between the vacuum manifold and the flexible membrane. Each of the vacuum channels includes a compliant conduit, a vacuum check valve, and suction cup. The vacuum check valve is biased to a closed condition and is configured to toggle from the closed condition to an open condition when a vacuum source applies a vacuum in the vacuum manifold and the suction cup of that vacuum channel is engaged by the object. Each of the check valves independently toggles between the closed condition and the open condition such that, when a vacuum is drawn in the vacuum manifold, the vacuum is maintained in the vacuum manifold and in the compliant conduits of the vacuum channels that have engaged suction cups.

Abstract: Vacuum seal packages can be classified based on image data. Training image data is received that includes image data about first vacuum seal packages. Labels associated with the first vacuum seal packages are received, where each of the labels includes a state of one of the first vacuum seal packages. A trained classification model is developed based on the training image data and the received labels. Image data representative of a second vacuum seal package is received. The image data is inputted into the trained classification model, where the trained classification model is configured to classify a state of the second vacuum seal package based on the image data. The state of the second vacuum seal package is received from the trained classification model.

Abstract: Vacuum seal packages can be classified based on image data. Training image data is received that includes image data about first vacuum seal packages. Labels associated with the first vacuum seal packages are received, where each of the labels includes a state of one of the first vacuum seal packages. A trained classification model is developed based on the training image data and the received labels. Image data representative of a second vacuum seal package is received. The image data is inputted into the trained classification model, where the trained classification model is configured to classify a state of the second vacuum seal package based on the image data. The state of the second vacuum seal package is received from the trained classification model.