正規化フローによる密度ベースの実行可能性学習：内省的なロボット組立に向けて
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
ロボット組立順序計画において、実行可能な例のみから正規化フローを用いたOOD検出として実行可能性を学習し、実行不可能な組立を検出する手法を提案した。
著者: Jianxiang Feng, Matan Atad, Ismael Rodríguez, Maximilian Durner, Stephan Günnemann, Rudolph Triebel
分類: cs.RO, cs.LG
原文アブストラクト
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.
