Papers › An Economic Framework for 6-DoF Grasp Detection

An Economic Framework for 6-DoF Grasp Detection

11 Jul 2024arXiv:2407.08366archive 2025-07-28

Xiao-Ming Wu, Jia-Feng Cai, Jian-Jian Jiang, Dian Zheng, Yi-Lin Wei, Wei-Shi Zheng

Robotic grasping in clutters is a fundamental task in robotic manipulation. In this work, we propose an economic framework for 6-DoF grasp detection, aiming to economize the resource cost in training and meanwhile maintain effective grasp performance. To begin with, we discover that the dense supervision is the bottleneck of current SOTA methods that severely encumbers the entire training overload, meanwhile making the training difficult to converge. To solve the above problem, we first propose an economic supervision paradigm for efficient and effective grasping. This paradigm includes a well-designed supervision selection strategy, selecting key labels basically without ambiguity, and an economic pipeline to enable the training after selection. Furthermore, benefit from the economic supervision, we can focus on a specific grasp, and thus we devise a focal representation module, which comprises an interactive grasp head and a composite score estimation to generate the specific grasp more accurately. Combining all together, the EconomicGrasp framework is proposed. Our extensive experiments show that EconomicGrasp surpasses the SOTA grasp method by about 3AP on average, and with extremely low resource cost, for about 1/4 training time cost, 1/8 memory cost and 1/30 storage cost. Our code is available at https://github.com/iSEE-Laboratory/EconomicGrasp.

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batch_viewpoint_params_to_matrix iSEE-Laboratory/EconomicGrasp/utils/loss_utils.py official repository ran MIT (permissive) · 84c2545d9172caa0 · report
collate_fn iSEE-Laboratory/EconomicGrasp/dataset/graspnet_dataset.py official repository ran MIT (permissive) · 78e44f3729f4608a · report
compute_graspness_loss iSEE-Laboratory/EconomicGrasp/models/loss_economicgrasp.py official repository ran MIT (permissive) · 702c7af5bf12d39e · report
compute_objectness_loss iSEE-Laboratory/EconomicGrasp/models/loss_economicgrasp.py official repository ran MIT (permissive) · 2e1a266839268c0c · report
generate_grasp_views iSEE-Laboratory/EconomicGrasp/utils/loss_utils.py official repository ran MIT (permissive) · 0f6be1306df35f18 · report
transform_point_cloud iSEE-Laboratory/EconomicGrasp/utils/loss_utils.py official repository ran MIT (permissive) · 8d4b2fff611706ec · report
set_bn_momentum_default iSEE-Laboratory/EconomicGrasp/libs/pointnet2/pytorch_utils.py official repository unverified MIT (permissive) · 0ad12b1e8408e2f9 · report

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