Robust Grasp Planning Over Uncertain Shape Completions

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

28 Sitaatiot (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.
OtsikkoProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
ISBN (elektroninen)978-1-7281-4004-9
DOI - pysyväislinkit
TilaJulkaistu - marrask. 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE/RSJ International Conference on Intelligent Robots and Systems - The Venetian Macao, Macau, Kiina
Kesto: 4 marrask. 20198 marrask. 2019


NimiProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
ISSN (painettu)2153-0858
ISSN (elektroninen)2153-0866


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems


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