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

Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly