General grasp-pose detector from the GraspNet team (SJTU): dense, temporally-smooth 7-DoF grasp poses on unseen objects from a single point cloud — reported ~93% bin-picking success, distributed as a license-gated SDK, free for research.
Input
point cloud
Output
6-DOF grasp pose
General grasp-pose detector from the GraspNet team (SJTU): dense, temporally-smooth 7-DoF grasp poses on unseen objects from a single point cloud — reported ~93% bin-picking success, distributed as a license-gated SDK, free for research.
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AnyGrasp, from the Machine Vision and Intelligence Group at Shanghai Jiao Tong University (the team behind the GraspNet-1Billion benchmark), predicts dense 7-DoF parallel-jaw grasp poses over full scenes from a single-view point cloud, with grasp quality scores and temporal smoothness for tracking grasps on moving objects. The T-RO 2023 paper reports human-comparable grasping with ~93% success in cluttered bin-picking of unseen objects. It is the most widely deployed academic grasp detector in research stacks (often chained after Grounding DINO + SAM for language-conditioned picking). Distribution is a compiled SDK with machine-locked license files — free for academic/research use, commercial licensing by contacting the authors.
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3D/Depth Camera (RGB-D) 1
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