用模仿学习控制气动夹爪，实现对易损禽类的精准抓举。
ChicGrasp: Imitation-Learning based Customized Dual-Jaw Gripper Control for Delicate, Irregular Bio-products Manipulation
- 基于50次示范数据，用条件扩散策略端到端规划五自由度动作
- 抓举成功率40.6%，完整流程耗时38秒，优于现有方法
- 适合农业机器人与仿生操作研究者参考
自动化家禽加工线仍依赖人工将滑腻、易损的整鸡抓举至挂具传送带。由于形变性强、解剖差异大且卫生要求严苛，传统吸力装置和预设动作不可靠。本文提出ChicGrasp，一种软硬件协同设计的端到端解决方案：独立驱动的双颚气动夹爪同时夹住鸡腿，配合从仅50组多视角遥操作示范（RGB + 本体感知）训练的条件扩散策略控制器，一次性规划包含夹爪动作在内的五自由度末端执行器运动。在单独呈现的生鲜肉鸡上，系统达成40.6%的抓举成功率，并在38秒内完成从拾取到挂具的全流程；而当前最先进的隐式行为克隆（IBC）和LSTM-GMM基线方法则完全失败。所有CAD设计、代码及数据集将开源。ChicGrasp证明了模仿学习可弥合刚性硬件与生物产品多样性之间的鸿沟，为农业工程与机器人学习领域提供可复现的基准与公开数据集。
原文摘要 · Abstract (English)
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. We present ChicGrasp, an end--to--end hardware--software co-design for this task. 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.
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