Kitting in the Wild through Online Domain Adaptation

2018-07-03 · Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci, Patric Jensfelt, Barbara Caputo

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.

📄 PDF Abstract BibTeX arXiv:1807.01028

Code (0)

등록된 구현이 없습니다.

Tasks

Domain Adaptation Object RecognitionOnline Domain Adaptation

A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery

2026-07-03 · Junhao Qiu, Jianjun Liu, Ting Liu, Rongjie Liao 외 arxiv

Multi-product kitting delivery imposes significant challenges for real-time scheduling in hybrid manufacturing systems that integrate processing and assembly, as dynamic order arrivals simultaneously alter supply depende…

Reinforcement Learning

Team O2AS at the World Robot Summit 2018: An Approach to Robotic Kitting and Assembly Tasks using General Purpose Grippers and Tools

2020-03-05 · Felix von Drigalski, Chisato Nakashima, Yoshiya Shibata, Yoshinori Konishi 외

We propose a versatile robotic system for kitting and assembly tasks which uses no jigs or commercial tool changers. Instead of specialized end effectors, it uses its two-finger grippers to grasp and hold tools to perfor…

Position

Semi-Supervised Domain Adaptation for Wildfire Detection

2024-04-02 · Jooyoung Jang, Youngseo Cha, Jisu Kim, SooHyung Lee 외

Recently, both the frequency and intensity of wildfires have increased worldwide, primarily due to climate change. In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adap…

Domain Adaptation object-detection Object Detection Semi-supervised Domain Adaptation

Sparse MLLM Anchors, Dense Adaptation: Breaking the Self-Referential Loop in Wild Test-Time Adaptation

2026-09-15 · Zhenbin Wang, Lei Zhang, Lituan Wang, Yan Wang 외 arxiv

Wild test-time adaptation (WTTA) updates a source model online under small test batches, concurrent distribution shifts, and time-varying class imbalance. Most WTTA methods derive their adaptation signals, including pred…

Test-time Adaptation

Turning Adaptation into Assets: Cross-Domain Bridging for Online Vision-Language Navigation

2026-05-22 · Zixuan Hu, Xuantuo Huang, Yancheng Li, Yichun Hu 외 arxiv

Navigating under non-stationary environment shifts poses a critical challenge for a Vision-and-Language Navigation (VLN) agent deployed in the wild. Yet, existing Test-Time Adaptation (TTA) methods for VLN largely treat …

Vision-Language Navigation Test-time Adaptation