Contact-GraspNet：混雑環境での効率的な6自由度把持生成
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
深度画像から点群を直接入力とし、把持接触点を基準に4自由度に次元削減した表現で6自由度パラレルジョー把持を生成するエンドツーエンドネットワークを提案。実環境の混雑場面で90%超の成功率を達成。
著者: Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, Dieter Fox
分類: cs.RO, cs.CV
原文アブストラクト
Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that possess several potential failure points and run-times unsuitable for closed-loop grasping. Therefore, we propose an end-to-end network that efficiently generates a distribution of 6-DoF parallel-jaw grasps directly from a depth recording of a scene. Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts. By rooting the full 6-DoF grasp pose and width in the observed point cloud, we can reduce the dimensionality of our grasp representation to 4-DoF which greatly facilitates the learning process. Our class-agnostic approach is trained on 17 million simulated grasps and generalizes well to real world sensor data. In a robotic grasping study of unseen objects in structured clutter we achieve over 90% success rate, cutting the failure rate in half compared to a recent state-of-the-art method.
