Results 11 to 20 of about 365,547 (358)
The GRASP Taxonomy of Human Grasp Types
IEEE Transactions on Human-Machine Systems, 2016 In this paper, we analyze and compare existing human grasp taxonomies and synthesize them into a single new taxonomy (dubbed “The GRASP Taxonomy” after the GRASP project funded by the European Commission). We consider only static and stable grasps performed by one hand.
Thomas Féix +2 more
exaly +4 more sources
Grasp Pose Detection in Point Clouds [PDF]
International Journal of Robotics Research, 2017 Recently, a number of grasp detection methods have been proposed that can be used to localize robotic grasp configurations directly from sensor data without estimating object pose.
Andreas Ten Pas +2 more
exaly +2 more sources
Language-driven Grasp Detection [PDF]
Computer Vision and Pattern RecognitionGrasp detection is a persistent and intricate challenge with various industrial applications. Recently, many meth-ods and datasets have been proposed to tackle the grasp detection problem. However, most of them do not consider using natural language as a
Vuong Dinh An +6 more
semanticscholar +3 more sources
Effect of grain shape on the dynamics of granular materials in 2D rotating drum [PDF]
EPJ Web of Conferences, 2021 We experimentally investigate the effect of the grain shape on the flow of granular material. The grain shape is modified to highlight the effect of grain circularity on granular flow in a 2D rotating drum.
Preud’homme Nicolas +3 more
doaj +1 more source
VTimeLLM: Empower LLM to Grasp Video Moments [PDF]
Computer Vision and Pattern Recognition, 2023 Large language models (LLMs) have shown remarkable text understanding capabilities, which have been ex-tended as Video LLMs to handle video data for compre-hending visual details.
Bin Huang +4 more
semanticscholar +1 more source
AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains [PDF]
IEEE Transactions on robotics, 2022 As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans. Our innate grasping system is prompt, accurate, flexible, and continuous across spatial and temporal domains.
Haoshu Fang +8 more
semanticscholar +1 more source
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation [PDF]
IEEE International Conference on Robotics and Automation, 2022 Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology. However, dexterous grasping is much more under-explored than object grasping with parallel grippers, partially due to ...
Ruicheng Wang +6 more
semanticscholar +1 more source
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes [PDF]
IEEE International Conference on Robotics and Automation, 2021 Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that ...
M. Sundermeyer +3 more
semanticscholar +1 more source
Deep Learning Approaches to Grasp Synthesis: A Review [PDF]
IEEE Transactions on robotics, 2022 Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping.
R. Newbury +11 more
semanticscholar +1 more source