52 citations · 134 across the 15 of their papers we have counts for
9 papers · 1 filter
Cosmos World Foundation Model Platform for Physical AI
NVIDIA, :, Niket Agarwal +76
Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present th…
DexTransfer: Real World Multi-fingered Dexterous Grasping with Minimal Human Demonstrations
Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao +4
Teaching a multi-fingered dexterous robot to grasp objects in the real world has been a challenging problem due to its high dimensional state and action space. We propose a robot-l…
RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks
Christopher Xie, Arsalan Mousavian, Yu Xiang +1
Segmenting unseen object instances in cluttered environments is an important capability that robots need when functioning in unstructured environments. While previous methods have…
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
Luyang Zhu, Arsalan Mousavian, Yu Xiang +4
Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to…
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
Keunhong Park, Arsalan Mousavian, Yu Xiang +1
Current 6D object pose estimation methods usually require a 3D model for each object. These methods also require additional training in order to incorporate new objects. As a resul…
The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation
Christopher Xie, Yu Xiang, Arsalan Mousavian +1
In order to function in unstructured environments, robots need the ability to recognize unseen novel objects. We take a step in this direction by tackling the problem of segmenting…