KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D input

Autor:	Chen, Yiye, Xu, Ruinian, Lin, Yunzhi, Chen, Hongyi, Vela, Patricio A.
Rok vydání:	2023
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We propose a new 6-DoF grasp pose synthesis approach from 2D/2.5D input based on keypoints. Keypoint-based grasp detector from image input has demonstrated promising results in the previous study, where the additional visual information provided by color images compensates for the noisy depth perception. However, it relies heavily on accurately predicting the location of keypoints in the image space. In this paper, we devise a new grasp generation network that reduces the dependency on precise keypoint estimation. Given an RGB-D input, our network estimates both the grasp pose from keypoint detection as well as scale towards the camera. We further re-design the keypoint output space in order to mitigate the negative impact of keypoint prediction noise to Perspective-n-Point (PnP) algorithm. Experiments show that the proposed method outperforms the baseline by a large margin, validating the efficacy of our approach. Finally, despite trained on simple synthetic objects, our method demonstrate sim-to-real capacity by showing competitive results in real-world robot experiments. Comment: Submitted to IROS2023; Code is available at: https://github.com/ivalab/KGN
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.05617 Zobrazit plný text záznamu View this record from Arxiv