发表机构
University of Stuttgart; Mercedes-Benz AG; Karlsruhe Institute of Technology (KIT); DLR Institute of Robotics and Mechatronics; Fraunhofer Institute for Industrial Engineering (IAO)（斯图加特大学; 梅赛德斯-奔驰公司; 卡尔斯鲁厄理工学院; 德国航空航天中心机器人与机电一体化研究所; 弗劳恩霍夫工业工程研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对工业料箱拣选需完全清空且无人工干预的挑战，提出四层分层混合方法，结合基于模型与无模型技术，通过在线自学习提升抓取性能，在汽车零件实验中实现100%料箱清空率，优于基线方法。
AI 中文摘要
料箱拣选是现代制造业的核心环节，但实现无需人工干预的料箱完全清空仍是关键挑战。基于模型的方法精度高，但当预定义抓取点被遮挡或感知失效时，常陷入死锁，且针对新零件通常需要耗费大量人力对抓取点进行微调以达到满意性能。无模型算法提供了更灵活的替代方案，具备“开箱即用”的通用性，但缺乏生产所需的可靠性和可重复性。与现有将两种技术孤立处理的研究不同，本文提出一种四层分层混合方法以结合两者优势：基于模型的流程作为鲁棒主干，无模型的“探索智能体”解决死锁情况并发现新抓取点；该方法由在线自学习机制支撑，该机制利用夹爪行程反馈和威尔逊得分区间自主对抓取候选点排序，减少人工调试工作量。在三种汽车零件上的验证表明，本文方法的抓取成功率显著优于无模型基线，且在所有实验中，基于模型基线的料箱清空率从50.9%提升至100%，这一完全清空料箱的转变标志着向真正自主、无干预的工业操作迈出了重要一步。
英文摘要
Bin-picking is a cornerstone of modern manufacturing, yet achieving complete bin clearance without manual intervention remains a critical challenge. While model-based methods provide high precision, they frequently suffer from deadlocks when predefined grasps are occluded or perception fails. Labor-intensive fine-tuning of grasp points is commonly required to reach a satisfactory performance for new parts. Model-free algorithms offer a more flexible alternative with "out-of-the-box" versatility but lack the reliability and repeatability required for production. Unlike existing work, which treats the two techniques in isolation, we propose a fourtiered hierarchical hybrid approach to combine the best of both worlds. A model-based pipeline serves as a robust backbone, while a model-free "exploration agent" resolves deadlock situations and discovers new grasp points. This is supported by an online self-learning mechanism that uses gripper-stroke feedback and Wilson score intervals to autonomously rank grasp candidates, reducing manual commissioning effort. Validation on three automotive parts demonstrates that our method significantly outperforms a model-free baseline in grasp success rate while improving the bin clearance rate of the model-based baseline from 50.9% to 100% across all experiments. This transition to full bin clearance marks a significant step towards truly autonomous, intervention-free industrial operation.
CommentsAccepted at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026. 8 pages, 7 figures
