不確実な形状補完に対するロバストな把持計画
Robust Grasp Planning Over Uncertain Shape Completions
部分観測から確率的に形状を補完し、その不確実性を考慮してロバストな把持を計画する手法を提案。シミュレーションと実機で把持品質と成功率の向上を示した。
著者: Jens Lundell, Francesco Verdoja, Ville Kyrki
分類: cs.RO
原文アブストラクト
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 200 grasps 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.
