A Two-stage Learning Architecture that Generates High-Quality Grasps for a Multi-Fingered Hand

Author:

Winkelbauer Dominik1,Bauml Berthold1,Humt Matthias1,Thuerey Nils2,Triebel Rudolph1

Affiliation:

1. DLR Institute of Robotics & Mechatronics,Germany

2. Technical University of Munich (TUM),Germany

Funder

Helmholtz Association under the joint research school “Munich School for Data Science - MUDS”

Publisher

IEEE

Reference41 articles.

1. Partial convex hull algorithms for efficient grasp quality evaluation

2. ShapeNet: An Information-Rich 3D Model Repository;chang;Stanford University - Princeton University - Toyota Technological Institute at Chicago,2015

3. An overview of dexterous manipulation

4. Algorithms for hyper-parameter optimization;bergstra;Advances in neural information processing systems,2011

5. Grasp planning: how to choose a suitable task wrench space

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1. DiPGrasp: Parallel Local Searching for Efficient Differentiable Grasp Planning;IEEE Robotics and Automation Letters;2024-10

2. Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand;2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids);2023-12-12

3. Self-Contained and Automatic Calibration of a Multi-Fingered Hand Using Only Pairwise Contact Measurements;2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids);2023-12-12

4. Learning-Based Real-Time Torque Prediction for Grasping Unknown Objects with a Multi-Fingered Hand;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

5. Shape Completion with Prediction of Uncertain Regions;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

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