ChicGrasp: Imitation-Learning based Customized Dual-Jaw Gripper Control for Delicate, Irregular Bio-products Manipulation

--Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor . Deformability, anatomical variance, and strict hygiene rules make conventional suction and scripted motions unreliable. An independently actuated dual-jaw pneumatic gripper clamps both chicken legs, while a conditional diffusion-policy controller, trained from only 50 multi-view teleoperation demonstrations (RGB + proprioception), plans 5-DoF end-effector motion, which includes jaw commands in one shot. On individually presented raw broiler carcasses, our system achieves a 40.6% grasp-and-lift success rate and completes the pick-to-shackle cycle in 38 s, whereas state-of-the-art implicit behaviour cloning (IBC) and LSTM-GMM baselines fail entirely. All CAD, code, and datasets will be open-source. ChicGrasp shows that imitation learning can bridge the gap between rigid hardware and variable bio-products, offering a reproducible benchmark and a public dataset for researchers in agricultural engineering and robot learning. OBOTS and intelligent agents are increasingly deployed in unstructured, dynamic environments where manual programming struggles to capture the intricacies of real-world tasks [1].