基于归一化流的密度可行性学习用于内省式机器人装配
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
AI总结：
本文提出一种仅需可行示例的基于归一化流密度估计的可行性学习方法，将机器人装配可行性学习建模为分布外检测，在检测不可行装配上优于单类基线并显著节省内存。
摘要：
机器人装配序列规划（RASP）中的机器学习（ML）模型需要对预测的解决方案具有内省能力，即判断其是否可行，以避免潜在的效率下降。以往的工作在训练时需要同时使用可行和不可行的示例。然而，当需要快速适应新产品变体而进行重新训练时，不可行示例很难充分收集。在这项工作中，我们提出了一种基于密度的可行性学习方法，该方法只需要可行示例。具体而言，我们将可行性学习问题形式化为使用归一化流（NF）的分布外（OOD）检测，归一化流是用于估计复杂概率分布的强大生成模型。在实验上，所提出的方法在机器人装配用例中得到了验证，并且在检测不可行装配方面优于其他单类基线方法。我们进一步研究了该方法的内在工作机制，并表明基于一种先进的归一化流变体可以获得大幅度的内存节省。
英文摘要：
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.
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。
