1 / 12

Learning to grasp objects with multiple contact points

This study explores robotic grasping techniques that leverage multiple contact points to enhance object manipulation. We analyze various features affecting grasp performance, including distance between contact points, depth variations, and issues caused by shadows. Our approach involves ranking grasp points using algorithms optimized for top pairs, aiming for effective collision detection and improved grasp point selection. Experimental results demonstrate the advantages of our optimization metrics and new features, showcasing the practical implications through offline experiments and video documentation.

wayne
Télécharger la présentation

Learning to grasp objects with multiple contact points

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Quoc V. Le, David Kamm, Arda Kara, Andrew Y. Ng Learning to grasp objects with multiple contact points

  2. Robotic grasping Saxena et al, IJRR (2008) Saxena et al, AAAI (2008) Barrett Hand

  3. Data Image data Depth data Quigley et al, ICRA 2009

  4. Features Good So So Bad

  5. Angle computation

  6. Problems Shadows Different objects

  7. More features • Distance between contact points • Depth variations and image variations • Collision detection

  8. Grasp point ranking • Use ranking algorithms for ranking grasp points • Optimize metric that focuses on the top pairs

  9. Experiments

  10. Offline experiments • Advantages of optimization metric • Advantages of new features

  11. Experimental results

  12. Videos

More Related