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Robotic Grasping

94 papers with code · 4 benchmarks · 22 datasets archive 2025-07-28

MiscellaneousRobots

This task is composed of using Deep Learning to identify how best to grasp objects using robotic arms in different scenarios. This is a very complex task as it might involve dynamic environments and objects unknown to the network.

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
GraspNet-1Billion (8 rows) FlexLoG-CD Rethinking 6-Dof Grasp Detection: A Flexible Framework for... — — Compare
Cornell Grasp Dataset (7 rows) grasp_det_seg_cnn (rgb only, IW split) End-to-end Trainable Deep Neural Network for Robotic Grasp... code — Compare
Jacquard dataset (3 rows) Efficient-Grasping Lightweight Convolutional Neural Network with Gaussian-based... — — Compare
NBMOD (1 row) RA-GraspNet (GraspNet with Rotation Anchor) NBMOD: Find It and Grasp It in Noisy Background code — Compare

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

22 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 94 papers with code (246 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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