8 papers
DOT-Sim: Differentiable Optical Tactile Simulation with Precise Real-to-Sim Physical Calibration
Yang You, Won Kyung Do, Aiden Swann +3
Simulating optical tactile sensors presents significant challenges due to their high deformability and intricate optical properties. To address these issues and enable a physically…
Robot Learning from a Physical World Model
Jiageng Mao, Sicheng He, Hao-Ning Wu +9
We introduce PhysWorld, a framework that enables robot learning from video generation through physical world modeling. Recent video generation models can synthesize photorealistic…
Robot Learning from Any Images
Siheng Zhao, Jiageng Mao, Wei Chow +11
We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a…
AllTracker: Efficient Dense Point Tracking at High Resolution
Adam W. Harley, Yang You, Xinglong Sun +11
We introduce AllTracker: a model that estimates long-range point tracks by way of estimating the flow field between a query frame and every other frame of a video. Unlike existing…
ArtGS:3D Gaussian Splatting for Interactive Visual-Physical Modeling and Manipulation of Articulated Objects
Qiaojun Yu, Xibin Yuan, Yu jiang +8
Articulated object manipulation remains a critical challenge in robotics due to the complex kinematic constraints and the limited physical reasoning of existing methods. In this wo…
Rodrigues Network for Learning Robot Actions
Jialiang Zhang, Haoran Geng, Yang You +4
Understanding and predicting articulated actions is important in robot learning. However, common architectures such as MLPs and Transformers lack inductive biases that reflect the…