Review of deep reinforcement learning-based object grasping: Techniques, open challenges, and recommendations
MQ Mohammed, KL Chung, CS Chyi - IEEE Access, 2020 - ieeexplore.ieee.org
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 …
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 …
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
developing models and algorithms enabling embodied agents to navigate and interact …
developing models and algorithms enabling embodied agents to navigate and interact …
Ifor: Iterative flow minimization for robotic object rearrangement
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-
world robotics applications in unstructured environments. We propose IFOR, Iterative Flow …
world robotics applications in unstructured environments. We propose IFOR, Iterative Flow …
Nerp: Neural rearrangement planning for unknown objects
Robots will be expected to manipulate a wide variety of objects in complex and arbitrary
ways as they become more widely used in human environments. As such, the …
ways as they become more widely used in human environments. As such, the …
STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems
Y Kantaros, MM Zavlanos - The International Journal of …, 2020 - journals.sagepub.com
This article proposes a new highly scalable and asymptotically optimal control synthesis
algorithm from linear temporal logic specifications, called STyLu S* for large-Scale optimal …
algorithm from linear temporal logic specifications, called STyLu S* for large-Scale optimal …
Structural concept learning via graph attention for multi-level rearrangement planning
M Kulshrestha, AH Qureshi - Conference on Robot Learning, 2023 - proceedings.mlr.press
Robotic manipulation tasks, such as object rearrangement, play a crucial role in enabling
robots to interact with complex and arbitrary environments. Existing work focuses primarily …
robots to interact with complex and arbitrary environments. Existing work focuses primarily …
Semantically grounded object matching for robust robotic scene rearrangement
Object rearrangement has recently emerged as a key competency in robot manipulation,
with practical solutions generally involving object detection, recognition, grasping and high …
with practical solutions generally involving object detection, recognition, grasping and high …