mlsanityData to 7 Oct 2026

ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation

arXiv 2303.16382 ↗First posted 29 Mar 2023cs.RO cs.CVcited by 18

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Abstract (arXiv)

This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Automation of operations in modern warehouses requires a robotic manipulator to deal with a wide variety of objects, unstructured storage, and dynamically changing inventory. Such settings pose challenges in perceiving the identity, physical characteristics, and state of objects during manipulation. Existing datasets for robotic manipulation consider a limited set of objects or utilize 3D models to generate synthetic scenes with limitation in capturing the variety of object properties, clutter, and interactions. We present a large-scale dataset collected in an Amazon warehouse using a robotic manipulator performing object singulation from containers with heterogeneous contents. ARMBench contains images, videos, and metadata that corresponds to 235K+ pick-and-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 41 of its 66 references that are in the map. First posted on arXiv 29 Mar 2023.

Builds on

41 of 66 references.

References · 41

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 3; 2025: 5; 2026: 5.

18 papers.

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  1. 2023RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunkingcited by 167
  2. 2023RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Modelscited by 12
  3. 2025GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenescited by 8
  4. 2023Large Scale Foundation Models for Intelligent Manufacturing Applications: A Surveycited by 6
  5. 2026Self-Refining Vision Language Model for Robotic Failure Detection and Reasoningcited by 6
  6. 2025Kaputt: A Large-Scale Dataset for Visual Defect Detectioncited by 5
  7. 2024Robot Instance Segmentation with Few Annotations for Graspingcited by 4
  8. 2023DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Graspingcited by 3
  9. 2024Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Graspingcited by 3
  10. 2024Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Searchcited by 3
  11. 2023High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinementcited by 1
  12. 2025SynRES: Towards Referring Expression Segmentation in the Wild via Synthetic Datacited by 1
6 more
  1. 2025Enhancing Video-Based Robot Failure Detection Using Task Knowledgecited by 0
  2. 2025RoboEye: Enhancing 2D Robotic Object Identification with Selective 3D Geometric Keypoint Matchingcited by 0
  3. 2026LabRobFail: A Benchmark for Robotic Failure Analysis in Chemical Self-driving Laboratorycited by 0
  4. 2026LogiScope-VQA: Benchmarking Vision-Language Models for Logistics Hazard Identification in Industrial Scenarioscited by 0
  5. 2026RoboVAD: A Large Cross-Domain Evaluation Benchmark for Anomaly Detection in Robotic Arm Manipulation Videoscited by 0
  6. 2026CoreSense: Traceable Failure Recall and Conflict-Aware Belief Gating for Auditable Robot Decisionscited by 0