{"ID":6023391,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T05:48:25.311437601Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05869","arxiv_id":"2607.05869","title":"GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation","abstract":"Robust robotic grasping of novel objects requires datasets that simultaneously provide photorealistic RGB-D observations, physically validated grasp quality annotations, and a principled bridge between simulation and the real world, which existing datasets lack to provide jointly. \\textbf{GraspIT} addresses this gap: tabletop scenes in NVIDIA Isaac Sim are annotated via a four-stage physical slip-test on parallel Franka Panda instances, producing trajectory-reachability checks and continuous quality scores beyond force-closure.Of ${\\sim}$2.3M candidates, 83% pass as \\emph{good} ($s{\\geq}0.50$); the 17% that passed force-closure but failed the slip-test provide graded hard negatives. A Real$\\leftrightarrow$Sim loop back-projects these labels onto 100 real-world scenes. The release provides ${\\sim}$316k annotated RGBD frame sets across 1035 sim and 100 real scenes, with instance masks, 6-DoF poses, physical object properties, and scored 6-DoF grasps. All tools are open-source and Docker-containerized. The trajectory planning within Isaac Sim further allows streaming of high resolution demonstrations for tabletop manipulation policy learning and behavior cloning.","short_abstract":"Robust robotic grasping of novel objects requires datasets that simultaneously provide photorealistic RGB-D observations, physically validated grasp quality annotations, and a principled bridge between simulation and the real world, which existing datasets lack to provide jointly. \\textbf{GraspIT} addresses this gap: t...","url_abs":"https://arxiv.org/abs/2607.05869","url_pdf":"https://arxiv.org/pdf/2607.05869v1","authors":"[\"Paul Koch. Adem Karakurt\",\"André Sers\"]","published":"2026-07-07T06:00:03Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\"]","methods":"[]","has_code":false}