Constrained Generative Sampling of 6-DoF Grasps

Jens Lundell, Francesco Verdoja, Tran Nguyen Le, Arsalan Mousavian, Dieter Fox, Ville Kyrki

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

1 Sitaatiot (Scopus)


Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable grasps is sufficient. However, for completing specific tasks, such as squeezing out liquid from a bottle, we want the grasp to be on a specific part of the object's body while avoiding other locations, such as the cap. This work presents a generative grasp sampling network, VCGS, capable of constrained 6-Degrees of Freedom (DoF) grasp sampling. In addition, we also curate a new dataset designed to train and evaluate methods for constrained grasping. The new dataset, called CONG, consists of over 14 million training samples of synthetically rendered point clouds and grasps at random target areas on 2889 objects. VCGS is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in simulation and on a real robot. The results demonstrate that VCGS achieves a 10-15% higher grasp success rate than the baseline while being 2-3 times as sample efficient. Supplementary material is available on our project website.

Otsikko2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
ISBN (elektroninen)9781665491907
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE/RSJ International Conference on Intelligent Robots and Systems
- Detroit, Yhdysvallat
Kesto: 1 lokak. 20235 lokak. 2023


NimiIEEE 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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