This paper presents a comprehensive survey on vision-based robotic gras**. We
conclude three key tasks during vision-based robotic gras**, which are object localization …

The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

How can we imbue robots with the ability to manipulate objects precisely but also to reason
about them in terms of abstract concepts? Recent works in manipulation have shown that …

Robotic manipulation can be formulated as inducing a sequence of spatial displacements:
where the space being moved can encompass an object, part of an object, or end effector. In …

In this work, we present FFB6D, a full flow bidirectional fusion network designed for 6D pose
estimation from a single RGBD image. Our key insight is that appearance information in the …

In this paper, we present a generalizable model-free 6-DoF object pose estimator called
Gen6D. Existing generalizable pose estimators either need the high-quality object models or …

Transparent objects are common in our daily life and frequently handled in the automated
production line. Robust vision-based robotic gras** and manipulation for these objects …

Humans form mental images of 3D scenes to support counterfactual imagination, planning,
and motor control. Our abilities to predict the appearance and affordance of the scene from …

As RGB-D sensors become more affordable, using RGB-D images to obtain high-accuracy
6D pose estimation results becomes a better option. State-of-the-art approaches typically …

Majority of the perception methods in robotics require depth information provided by RGB-D
cameras. However, standard 3D sensors fail to capture depth of transparent objects due to …