# OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning

arXiv: https://arxiv.org/abs/1911.06487 · HTML: https://mlsanity.com/paper/1911.06487/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

## Built on by (most cited first, 28 of 28)

- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [Avalanche: an End-to-End Library for Continual Learning](https://mlsanity.com/paper/2104.00405/) (2021)
- [Online Continual Learning for Embedded Devices](https://mlsanity.com/paper/2203.10681/) (2022)
- [CLAD: A realistic Continual Learning benchmark for Autonomous Driving](https://mlsanity.com/paper/2210.03482/) (2022)
- [Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast](https://mlsanity.com/paper/2106.03796/) (2021)
- [Learning the Superpixel in a Non-iterative and Lifelong Manner](https://mlsanity.com/paper/2103.10681/) (2021)
- [F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning](https://mlsanity.com/paper/2103.12242/) (2021)
- [Online Continual Learning for Robust Indoor Object Recognition](https://mlsanity.com/paper/2307.09827/) (2023)
- [A Benchmark and Empirical Analysis for Replay Strategies in Continual Learning](https://mlsanity.com/paper/2208.02660/) (2022)
- [PLAN: Proactive Low-Rank Allocation for Continual Learning](https://mlsanity.com/paper/2510.21188/) (2025)
- [Continual Learning for Autonomous Robots: A Prototype-based Approach](https://mlsanity.com/paper/2404.00418/) (2024)
- [Continual Hyperbolic Learning of Instances and Classes](https://mlsanity.com/paper/2506.10710/) (2025)
- [3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding](https://mlsanity.com/paper/2212.06833/) (2022)
- [OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams](https://mlsanity.com/paper/2308.15059/) (2023)
- [Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search](https://mlsanity.com/paper/2407.07541/) (2024)
- [Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images](https://mlsanity.com/paper/2203.02571/) (2022)
- [On the challenges to learn from Natural Data Streams](https://mlsanity.com/paper/2301.03495/) (2023)
- [EdgeOL: Efficient in-situ Online Learning on Edge Devices](https://mlsanity.com/paper/2401.16694/) (2024)
- [TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection](https://mlsanity.com/paper/2409.16215/) (2024)
- [RECALL: Rehearsal-free Continual Learning for Object Classification](https://mlsanity.com/paper/2209.14774/) (2022)
- [RF-Annotate: Automatic RF-Supervised Image Annotation of Common Objects in Context](https://mlsanity.com/paper/2211.08837/) (2022)
- [Explainable Lifelong Stream Learning Based on "Glocal" Pairwise Fusion](https://mlsanity.com/paper/2306.13410/) (2023)
- [Detecting Morphing Attacks via Continual Incremental Training](https://mlsanity.com/paper/2307.15105/) (2023)
- [A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation](https://mlsanity.com/paper/2308.00287/) (2023)
- [Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics](https://mlsanity.com/paper/2407.06450/) (2024)
- [Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Restructuring](https://mlsanity.com/paper/2511.20679/) (2025)
- [Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network](https://mlsanity.com/paper/2511.01553/) (2025)
- [Active Continual Learning with Metaplastic Binary Bayesian Neural Networks](https://mlsanity.com/paper/2605.30198/) (2026)

## Abstract (arXiv)

The recent breakthroughs in computer vision have benefited from the availability of large representative datasets (e.g. ImageNet and COCO) for training. Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks. Fully retraining models each time a new task becomes available is infeasible due to computational, storage and sometimes privacy issues, while na"ive incremental strategies have been shown to suffer from catastrophic forgetting. It is crucial for the robots to operate continuously under open-set and detrimental conditions with adaptive visual perceptual systems, where lifelong learning is a fundamental capability. However, very few datasets and benchmarks are available to evaluate and compare emerging techniques. To fill this gap, we provide a new lifelong robotic vision dataset ("OpenLORIS-Object") collected via RGB-D cameras. The dataset embeds the challenges faced by a robot in the real-life application and provides new benchmarks for validating lifelong object recognition algorithms. Moreover, we have provided a testbed of 9 state-of-the-art lifelong learning algorithms. Each of them involves 48 tasks with 4 evaluation metrics over the OpenLORIS-Object dataset. The results demonstrate that the object recognition task in the ever-changing difficulty environments is far from being solved and the bottlenecks are at the forward/backward transfer designs. Our dataset and benchmark are publicly available at at hrefhttps://lifelong-robotic-vision.github.io/dataset/objectunderlinehttps://lifelong-robotic-vision.github.io/dataset/object.