ChicGrasp：基于模仿学习的定制化双爪夹具控制用于精细、不规则生物产品操作
ChicGrasp: Imitation-Learning based Customized Dual-Jaw Gripper Control for Delicate, Irregular Bio-products Manipulation
AI总结：
ChicGrasp通过模仿学习实现定制化双爪夹具控制，有效提升对精细、不规则生物产品的操作效率。
摘要：
自动化家禽加工线仍然依赖人类将滑腻、易 bruised 的 carcasses 放到 shackles 链传送带上。变形性、解剖差异和严格的卫生规则使得传统吸盘和预设动作不可靠。我们提出了 ChicGrasp，一种为该任务设计的端到端硬件软件联合设计。一个独立驱动的双爪气动夹具夹住鸡的双腿，同时一个条件扩散策略控制器，仅通过50次多视角遥控演示（RGB + 本体感觉）训练，计划5个自由度末端执行器运动，包括一次 jaw 命令。在单独展示的生鸡 carcasses 上，我们的系统实现了40.6%的抓取和提升成功率，并在38秒内完成从挑选到链传送的循环，而最先进的隐式行为克隆（IBC）和LSTM-GMM基线完全失败。所有CAD、代码和数据集都将开源。ChicGrasp表明模仿学习可以弥补刚性硬件与可变生物产品之间的差距，提供可重复的基准和公开数据集供农业工程和机器人学习研究者使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
