Review of deep reinforcement learning-based object gras**: Techniques, open challenges, and recommendations
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 …
reinforcement learning-based object manipulation. Various studies are examined through a …
Cosypose: Consistent multi-view multi-object 6d pose estimation
We introduce an approach for recovering the 6D pose of multiple known objects in a scene
captured by a set of input images with unknown camera viewpoints. First, we present a …
captured by a set of input images with unknown camera viewpoints. First, we present a …
Algorithms and systems for manipulating multiple objects
Robot manipulation of multiple objects is an important topic for applications including
warehouse automation, service robots performing cleaning, and large-scale object sorting …
warehouse automation, service robots performing cleaning, and large-scale object sorting …
Visual room rearrangement
There has been a significant recent progress in the field of Embodied AI with researchers
develo** models and algorithms enabling embodied agents to navigate and interact …
develo** models and algorithms enabling embodied agents to navigate and interact …
Lego-net: Learning regular rearrangements of objects in rooms
Humans universally dislike the task of cleaning up a messy room. If machines were to help
us with this task, they must understand human criteria for regular arrangements, such as …
us with this task, they must understand human criteria for regular arrangements, such as …
Selective object rearrangement in clutter
We propose an image-based, learned method for selective tabletop object rearrangement in
clutter using a parallel jaw gripper. Our method consists of three stages: graph-based object …
clutter using a parallel jaw gripper. Our method consists of three stages: graph-based object …