  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.
