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DexYCB: A Benchmark for Capturing Hand Grasping of Objects

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arxiv 2104.04631 v1 pith:RBT3BCPO submitted 2021-04-09 cs.CV

classification cs.CV
keywords dexycbhandobjectbenchmarkcapturingdatasetestimationgrasping
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough benchmark of state-of-the-art approaches on three relevant tasks: 2D object and keypoint detection, 6D object pose estimation, and 3D hand pose estimation. Finally, we evaluate a new robotics-relevant task: generating safe robot grasps in human-to-robot object handover. Dataset and code are available at https://dex-ycb.github.io.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Video to Control: A Survey of Learning Manipulation Interfaces from Temporal Visual Data

    cs.RO 2026-04 accept novelty 6.0 of 10

    Video-to-robot control methods cluster into three interface families, and the field’s main bottleneck is grounding video-derived predictions into dependable closed-loop robot behavior.

  2. Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets

    cs.IR 2025-05 conditional novelty 5.0 of 10

    A curation pipeline with a shared data model, a repository, a knowledge graph, and a ChatGPT-based chatbot that lets researchers ask questions across human-robot datasets.

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