SimPLE, a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects
Existing robotic systems have a tension between generality and precision. Deployed
solutions for robotic manipulation tend to fall into the paradigm of one robot solving a single …
solutions for robotic manipulation tend to fall into the paradigm of one robot solving a single …
Development of robotic bin picking platform with cluttered objects using human guidance and convolutional neural network (CNN)
Industrial robots have been utilized for factory automation due to their high repeatability.
Along with the development of visual servo and machine learning techniques, various vision …
Along with the development of visual servo and machine learning techniques, various vision …
MetaGraspNetV2: All-in-one dataset enabling fast and reliable robotic bin picking via object relationship reasoning and dexterous grasping
Grasping unknown objects in unstructured environments is one of the most challenging and
demanding tasks for robotic bin picking systems. Developing a holistic approach is crucial to …
demanding tasks for robotic bin picking systems. Developing a holistic approach is crucial to …
Autonomous robotic bin picking platform generated from human demonstration and YOLOv5
Vision-based robots have been utilized for pick-and-place operations by their ability to find
object poses. As they progress into handling a variety of objects with cluttered state, more …
object poses. As they progress into handling a variety of objects with cluttered state, more …
Metagraspnet: A large-scale benchmark dataset for scene-aware ambidextrous bin picking via physics-based metaverse synthesis
Autonomous bin picking poses significant challenges to vision-driven robotic systems given
the complexity of the problem, ranging from various sensor modalities, to highly entangled …
the complexity of the problem, ranging from various sensor modalities, to highly entangled …
Cluttered food grasping with adaptive fingers and synthetic-data trained object detection
A Ummadisingu, K Takahashi… - … Conference on Robotics …, 2022 - ieeexplore.ieee.org
The food packaging industry handles an immense variety of food products with wide-ranging
shapes and sizes, even within one kind of food. Menus are also diverse and change …
shapes and sizes, even within one kind of food. Menus are also diverse and change …
Sim-to-real grasp detection with global-to-local rgb-d adaptation
This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a
domain adaptation problem. In this case, we present a global-to-local method to address …
domain adaptation problem. In this case, we present a global-to-local method to address …
Application of Bayesian Optimization in Gripper Design for Effective Grasping
Despite many recent technological advancements, grasping remains a challenging open
problem in robotic manipulation. In contrast with most research which focuses equipping …
problem in robotic manipulation. In contrast with most research which focuses equipping …
SYN-PBOX: A large-scale benchmark dataset of box-shaped objects for scene understanding of bin picking
J Cui, J Shi, C Liu, S Bai, X Shu, X Zuo, Q Qian, D Xu… - Neurocomputing, 2025 - Elsevier
Robotic grasping and sorting tasks require a large amount of comprehensive and high-
quality data, but collecting real-world data is too expensive and time-consuming in practice …
quality data, but collecting real-world data is too expensive and time-consuming in practice …
[HTML][HTML] Physics-Based Self-Supervised Grasp Pose Detection
Current industrial robotic manipulators have made their lack of flexibility evident. The
systems must know beforehand the piece and its position. To address this issue …
systems must know beforehand the piece and its position. To address this issue …