# Kitting in the Wild through Online Domain Adaptation

arXiv: https://arxiv.org/abs/1807.01028 · HTML: https://mlsanity.com/paper/1807.01028/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 24 of 24)

- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation](https://mlsanity.com/paper/2208.05117/) (2022)
- [Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2108.11249/) (2021)
- [GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation](https://mlsanity.com/paper/2207.09763/) (2022)
- [TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation](https://mlsanity.com/paper/2303.16730/) (2023)
- [On the Road to Online Adaptation for Semantic Image Segmentation](https://mlsanity.com/paper/2203.16195/) (2022)
- [Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts](https://mlsanity.com/paper/2308.08810/) (2023)
- [Evaluation of Test-Time Adaptation Under Computational Time Constraints](https://mlsanity.com/paper/2304.04795/) (2023)
- [Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation](https://mlsanity.com/paper/2402.17316/) (2024)
- [Test-Time Model Adaptation for Quantized Neural Networks](https://mlsanity.com/paper/2508.02180/) (2025)
- [Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation](https://mlsanity.com/paper/2103.14496/) (2021)
- [Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection](https://mlsanity.com/paper/2106.03496/) (2021)
- [Burn After Reading: Online Adaptation for Cross-domain Streaming Data](https://mlsanity.com/paper/2112.04345/) (2021)
- [A Multi-stage Framework with Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.00003/) (2022)
- [Generalized Robust Test-Time Adaptation in Continuous Dynamic Scenarios](https://mlsanity.com/paper/2310.04714/) (2023)
- [Test-Time Adaptation for Combating Missing Modalities in Egocentric Videos](https://mlsanity.com/paper/2404.15161/) (2024)
- [Dual-level Adaptation for Multi-Object Tracking: Building Test-Time Calibration from Experience and Intuition](https://mlsanity.com/paper/2603.21629/) (2026)
- [On the Challenges of Open World Recognitionunder Shifting Visual Domains](https://mlsanity.com/paper/2107.04461/) (2021)
- [Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics](https://mlsanity.com/paper/2407.06450/) (2024)
- [ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking](https://mlsanity.com/paper/2509.02182/) (2025)
- [Exploring Data Aggregation and Transformations to Generalize across Visual Domains](https://mlsanity.com/paper/2108.09208/) (2021)
- [Contrastive Learning for Cross-Domain Open World Recognition](https://mlsanity.com/paper/2203.09257/) (2022)
- [SloMo-Fast: Slow-Momentum and Fast-Adaptive Teachers for Source-Free Continual Test-Time Adaptation](https://mlsanity.com/paper/2511.18468/) (2025)
- [Efficient Test-Time Adaptation For Robot Policies](https://mlsanity.com/paper/2610.20318/) (2026)

## Abstract (arXiv)

Technological developments call for increasing perception and action capabilities of robots. Among other skills, vision systems that can adapt to any possible change in the working conditions are needed. Since these conditions are unpredictable, we need benchmarks which allow to assess the generalization and robustness capabilities of our visual recognition algorithms. In this work we focus on robotic kitting in unconstrained scenarios. As a first contribution, we present a new visual dataset for the kitting task. Differently from standard object recognition datasets, we provide images of the same objects acquired under various conditions where camera, illumination and background are changed. This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified. Our second contribution is a novel online adaptation algorithm for deep models, based on batch-normalization layers, which allows to continuously adapt a model to the current working conditions. Differently from standard domain adaptation algorithms, it does not require any image from the target domain at training time. We benchmark the performance of the algorithm on the proposed dataset, showing its capability to fill the gap between the performances of a standard architecture and its counterpart adapted offline to the given target domain.