43 citations · 43 across the 5 of their papers we have counts for
6 papers
DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Library
Youhui Wang, Yunzhu Li, Li Fei-Fei +2
Human videos offer a scalable source of demonstrations for dexterous robot manipulation. However, existing human-to-simulation-to-robot (Human2Sim2Robot) pipelines rely on predefin…
IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction
Yingke Wang, Hao Li, Yifeng Zhu +6
Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke…
RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing
Bo Ai, Stephen Tian, Haochen Shi +4
Tactile feedback is critical for understanding the dynamics of both rigid and deformable objects in many manipulation tasks, such as non-prehensile manipulation and dense packing.…
Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussians
Licheng Zhong, Hong-Xing Yu, Jiajun Wu +1
Reconstructing and simulating elastic objects from visual observations is crucial for applications in computer vision and robotics. Existing methods, such as 3D Gaussians, model 3D…
Model-Based Control with Sparse Neural Dynamics
Ziang Liu, Genggeng Zhou, Jeff He +4
Learning predictive models from observations using deep neural networks (DNNs) is a promising new approach to many real-world planning and control problems. However, common DNNs ar…
Learning Physical Graph Representations from Visual Scenes
Daniel M. Bear, Chaofei Fan, Damian Mrowca +8
Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, an…