Robust Grasp Planning Over Uncertain Shape Completions

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27 Citations (Scopus)


We present a method for planning robust grasps over uncertain shape completed objects. For shape completion, a deep neural network is trained to take a partial view of the object as input and outputs the completed shape as a voxel grid. The key part of the network is dropout layers which are enabled not only during training but also at run-time to generate a set of shape samples representing the shape uncertainty through Monte Carlo sampling. Given the set of shape completed objects, we generate grasp candidates on the mean object shape but evaluate them based on their joint performance in terms of analytical grasp metrics on all the shape candidates. We experimentally validate and benchmark our method against another state-of-the-art method with a Barrett hand on 90000 grasps in simulation and 100 on a real Franka Emika Panda. All experimental results show statistically significant improvements both in terms of grasp quality metrics and grasp success rate, demonstrating that planning shape-uncertainty-aware grasps brings significant advantages over solely planning on a single shape estimate, especially when dealing with complex or unknown objects.
Original languageEnglish
Title of host publicationProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Number of pages7
ISBN (Electronic)978-1-7281-4004-9
Publication statusPublished - Nov 2019
MoE publication typeA4 Conference publication
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - The Venetian Macao, Macau, China
Duration: 4 Nov 20198 Nov 2019

Publication series

NameProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
Internet address


  • Dexterous manipulators
  • Grippers
  • Humanoid robots
  • Learning (artificial intelligence)
  • Monte Carlo method
  • Neural nets
  • Robot vision
  • Shape recognition


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