用机器学习优化机器人抓取策略，降低20%失败率。
Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
- 通过预测调整参数和吸盘选择提升抓取成功率。
- 在超200万次抓取中，失败率降低20%。
- 适合大规模仓储自动化场景中的机器人系统优化。
仓库自动化对提升运营效率、降低成本及应对人力波动至关重要。以往研究多聚焦于使用启发式方法采样抓取任务，并通过机器学习预测成功概率，但较少关注如何利用数据驱动方法直接优化采样策略以实现规模化性能提升。本文提出一种基于机器学习的框架，通过预测变换调整及多吸盘末端执行器的吸盘选择，优化采样抓取，从而提高成功率。该框架在模拟亚马逊机器人诱导（Robin）车队操作的测试工位中集成并评估，覆盖超过200万次抓取。结果表明，相比基于启发式的采样基线，该方法将抓取失败率降低20%，验证了其在大规模仓库自动化场景中的有效性。
原文摘要 · Abstract (English)
Warehouse automation plays a pivotal role in enhancing operational efficiency, minimizing costs, and improving resilience to workforce variability. While prior research has demonstrated the potential of machine learning (ML) models to increase picking success rates in large-scale robotic fleets by prioritizing high-probability picks and packages, these efforts primarily focused on predicting success probabilities for picks sampled using heuristic methods. Limited attention has been given, however, to leveraging data-driven approaches to directly optimize sampled picks for better performance at scale. In this study, we propose an ML-based framework that predicts transform adjustments as well as improving the selection of suction cups for multi-suction end effectors for sampled picks to enhance their success probabilities. The framework was integrated and evaluated in test workcells that resemble the operations of Amazon Robotics' Robot Induction (Robin) fleet, which is used for package manipulation. Evaluated on over 2 million picks, the proposed method achieves a 20\% reduction in pick failure rates compared to a heuristic-based pick sampling baseline, demonstrating its effectiveness in large-scale warehouse automation scenarios.
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