融合模型与无模型方法，实现工业料箱抓取全自动清空。
Picking Bins Empty: A Hierarchical Hybrid Approach with Online Self-Learning of Grasp Points for Reliable Industrial Bin-Picking
- 分层混合架构，用模型方法为主、无模型探索为辅解决抓取死锁。
- 自学习机制通过夹爪动作反馈自动排序抓点，提升成功率至100%。
- 适用于需高可靠性的自动化产线，减少人工调参成本。
料箱抓取是现代制造的核心技术，但实现完全清空而无需人工干预仍是重大挑战。基于模型的方法精度高，但在抓点被遮挡或感知失败时易陷入死锁，常需大量人工调参才能达到理想性能。无模型算法虽具即插即用的灵活性，却缺乏生产所需的可靠性与重复性。不同于以往将两者割裂的做法，本文提出四层分层混合方法，以模型方法为骨干，引入无模型‘探索代理’解决死锁并发现新抓点。该方法依托在线自学习机制，利用夹爪动作反馈与威尔逊区间对抓点候选进行自主排序，显著降低人工配置工作量。在三个汽车零部件上的验证表明，本方法在抓取成功率上显著优于无模型基线；同时将基于模型的基线清空率从50.9%提升至100%，实现了真正的全料箱清空，标志着向完全自主、免干预工业操作迈出关键一步。
原文摘要 · Abstract (English)
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.
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