Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Khargonkar, Ninad"'
We introduce a novel representation named as the unified gripper coordinate space for grasp synthesis of multiple grippers. The space is a 2D surface of a sphere in 3D using longitude and latitude as its coordinates, and it is shared for all robotic
Externí odkaz:
http://arxiv.org/abs/2409.14519
We introduce a large-scale dataset named MultiGripperGrasp for robotic grasping. Our dataset contains 30.4M grasps from 11 grippers for 345 objects. These grippers range from two-finger grippers to five-finger grippers, including a human hand. All gr
Externí odkaz:
http://arxiv.org/abs/2403.09841
Autor:
Qian, Howard H., Lu, Yangxiao, Ren, Kejia, Wang, Gaotian, Khargonkar, Ninad, Xiang, Yu, Hang, Kaiyu
In order to successfully perform manipulation tasks in new environments, such as grasping, robots must be proficient in segmenting unseen objects from the background and/or other objects. Previous works perform unseen object instance segmentation (UO
Externí odkaz:
http://arxiv.org/abs/2403.01731
Autor:
Khargonkar, Ninad, Allu, Sai Haneesh, Lu, Yangxiao, P, Jishnu Jaykumar, Prabhakaran, Balakrishnan, Xiang, Yu
We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results ar
Externí odkaz:
http://arxiv.org/abs/2306.15620
Skeletonization has been a popular shape analysis technique that models both the interior and exterior of an object. Existing template-based calculations of skeletal models from anatomical structures are a time-consuming manual process. Recently, lea
Externí odkaz:
http://arxiv.org/abs/2303.02123
Autor:
Lu, Yangxiao, Khargonkar, Ninad, Xu, Zesheng, Averill, Charles, Palanisamy, Kamalesh, Hang, Kaiyu, Guo, Yunhui, Ruozzi, Nicholas, Xiang, Yu
We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches either grasp or push an object and then obtain the segmentation mask o
Externí odkaz:
http://arxiv.org/abs/2302.03793
We introduce a neural implicit representation for grasps of objects from multiple robotic hands. Different grasps across multiple robotic hands are encoded into a shared latent space. Each latent vector is learned to decode to the 3D shape of an obje
Externí odkaz:
http://arxiv.org/abs/2207.02959
Information-theoretic quantities like entropy and mutual information have found numerous uses in machine learning. It is well known that there is a strong connection between these entropic quantities and submodularity since entropy over a set of rand
Externí odkaz:
http://arxiv.org/abs/2006.15412
Autor:
Annaswamy, Thiru M., Pradhan, Gaurav N., Chakka, Keerthana, Khargonkar, Ninad, Borresen, Aleks, Prabhakaran, Balakrishnan
Publikováno v:
In Physical Medicine & Rehabilitation Clinics of North America May 2021 32(2):437-449
Publikováno v:
Journal of Healthcare Informatics Research; Jun2023, Vol. 7 Issue 2, p225-253, 29p