In recent years, modern techniques in deep learning and large-scale datasets
have led to impressive progress in 3D instance segmentation, grasp pose
estimation, and robotics. This allows for accurate detection directly in 3D
scenes, object- and environment-aware grasp prediction, as well as robust and
repeatable robotic manipulation. This work aims to integrate these recent
methods into a comprehensive framework for robotic interaction and manipulation
in human-centric environments. Specifically, we leverage 3D reconstructions
from a commodity 3D scanner for open-vocabulary instance segmentation,
alongside grasp pose estimation, to demonstrate dynamic picking of objects, and
opening of drawers. We show the performance and robustness of our model in two
sets of real-world experiments including dynamic object retrieval and drawer
opening, reporting a 51% and 82% success rate respectively. Code of our
framework as well as videos are available on: this https URL

将现代深度学习技术和大规模数据集应用于 3D 实例分割、抓取姿态估计和机器人学领域，以构建一个综合框架，实现在以人为中心的环境中的机器人交互和操控。

Spot-Compose: 点云中的开放词汇物体检索和抽屉操作框架

Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and  Drawer Manipulation in Point Clouds

Grasp pose estimation is an important issue for robots to interact with the
real world. However, most of existing methods require exact 3D object models
available beforehand or a large amount of grasp annotations for training. To
avoid these problems, we propose TransGrasp, a category-level grasp pose
estimation method that predicts grasp poses of a category of objects by
labeling only one object instance. Specifically, we perform grasp pose transfer
across a category of objects based on their shape correspondences and propose a
grasp pose refinement module to further fine-tune grasp pose of grippers so as
to ensure successful grasps. Experiments demonstrate the effectiveness of our
method on achieving high-quality grasps with the transferred grasp poses. Our
code is available at this https URL

该研究提出了 TransGrasp，一种基于类别级别的抓取姿态估计方法，可以通过标记一个物体实例来预测一个物体类别的抓取姿态，并通过抓取姿态迁移实现抓取动作。实验表明，该方法可以实现高质量的抓取，并提供了源代码。