This PhD project tackles the challenge of how to deal with variation, deformability and flexibility in agro-food. Many harvesting and food processing tasks require two separate robots, e.g., when the product needs to be grasped first before it can be manipulated (bimanual manipulation). Efficient processing requires a joint optimization of the overall plan and runtime coordination of the perception and control in both arms.
During this PhD, you will develop control approaches capable of dealing with the intricate and uncertain interaction mechanics during processing natural products. You will investigate the boundary between control theory and machine learning, so as to equip robots with the ability of performing these actions with the necessary flexibility, but without learning times incompatible with practical implementations.
This project will have real-world validation within agricultural and food processing robotics, in the context of the FlexCRAFT project (https://flexcraftprogram.com/ ).
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