Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i.e. whether they are feasible or not, to circumvent potential efficiency degradation. Previous works need both feasible and infeasible examples during training. However, the infeasible ones are hard to collect sufficiently when re-training is required for swift adaptation to new product variants. In this work, we propose a density-based feasibility learning method that requires only feasible examples. Concretely, we formulate the feasibility learning problem as Out-of-Distribution (OOD) detection with Normalizing Flows (NF), which are powerful generative models for estimating complex probability distributions. Empirically, the proposed method is demonstrated on robotic assembly use cases and outperforms other single-class baselines in detecting infeasible assemblies. We further investigate the internal working mechanism of our method and show that a large memory saving can be obtained based on an advanced variant of NF.

翻译：机器学习模型在机器人装配序列规划中需要对其预测的解决方案具备自省能力，即判断方案是否可行，以避免潜在效率退化。现有方法在训练过程中需要同时使用可行与不可行的样本实例。然而，当需要快速适配新产品变体进行模型重训练时，不可行样本难以充分收集。本文提出一种仅需可行样本的基于密度的可行性学习方法。具体而言，我们将可行性学习问题形式化为基于归一化流（一种用于估计复杂概率分布的强大生成模型）的分布外检测任务。实验证明，所提方法在机器人装配用例中能够有效检测不可行装配序列，性能优于其他单类基线方法。我们进一步探究了该方法的内部工作机制，并表明基于归一化流的先进变体可实现显著的内存节省。