Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …

We propose a new method named OnePose for object pose estimation. Unlike existing
instance-level or category-level methods, OnePose does not rely on CAD models and can …

Bridging thereality gap'that separates simulated robotics from experiments on hardware
could accelerate robotic research through improved data availability. This paper explores …

A key challenge in scaling up robot learning to many skills and environments is removing
the need for human supervision, so that robots can collect their own data and improve their …

This paper presents a novel approach to estimating the continuous six degree of freedom (6-
DoF) pose (3D translation and rotation) of an object from a single RGB image. The approach …

We review the work on data-driven grasp synthesis and the methodologies for sampling and
ranking candidate grasps. We divide the approaches into three groups based on whether …

We consider the problem of detecting robotic grasps in an RGB-D view of a scene
containing objects. In this work, we apply a deep learning approach to solve this problem …

This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a
hierarchical sparse coding technique for learning features from 3D point cloud data. HMP3D …

We present MOPED, a framework for Multiple Object Pose Estimation and Detection that
seamlessly integrates single-image and multi-image object recognition and pose estimation …

Resource constrained mobile devices need to leverage computation on nearby servers to
run responsive applications that recognize objects, people, or gestures from real-time video …