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

arXiv: https://arxiv.org/abs/2307.01317 · HTML: https://mlsanity.com/paper/2307.01317/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Why Normalizing Flows Fail to Detect Out-of-Distribution Data](https://mlsanity.com/paper/2006.08545/) (2020)
- [Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows](https://mlsanity.com/paper/2008.12577/) (2020)
- [Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts](https://mlsanity.com/paper/2006.09239/) (2020)
- [Hybrid Models for Open Set Recognition](https://mlsanity.com/paper/2003.12506/) (2020)
- [normflows: A PyTorch Package for Normalizing Flows](https://mlsanity.com/paper/2302.12014/) (2023)
- [Resampling Base Distributions of Normalizing Flows](https://mlsanity.com/paper/2110.15828/) (2021)
- [A System-Level View on Out-of-Distribution Data in Robotics](https://mlsanity.com/paper/2212.14020/) (2022)
- [Estimating Model Uncertainty of Neural Networks in Sparse Information Form](https://mlsanity.com/paper/2006.11631/) (2020)
- [CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN](https://mlsanity.com/paper/2207.07553/) (2022)
- [Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning](https://mlsanity.com/paper/2211.01576/) (2022)
- [Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking](https://mlsanity.com/paper/2203.10568/) (2022)
- [Planning Assembly Sequence with Graph Transformer](https://mlsanity.com/paper/2210.05236/) (2022)
- [Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks](https://mlsanity.com/paper/2109.12869/) (2021)
- [Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning](https://mlsanity.com/paper/2303.10135/) (2023)

## Built on by (most cited first, 2 of 2)

- [Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning](https://mlsanity.com/paper/2311.06481/) (2023)
- [FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference](https://mlsanity.com/paper/2407.15161/) (2024)

## Abstract (arXiv)

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.