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

Anonymous

Keywords: Robotic Assembly Sequence Planning, Feasibility Learning, Robotic Introspection, Normalizing Flows

TL;DR: We propose a density-based method for feasibility learning problem in Robotic Assembly based on only feasible examples.

Abstract: 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. While previous works tackling this problem are in need of both feasible and infeasible examples, which is hard to prepare sufficiently in practice, 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 baselines in detecting infeasible assemblies. We further investigate the internal working mechanism of our method and show that a large memory savings can be obtained based on an advanced variant of NF.