1 citations · 3 across the 9 of their papers we have counts for
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BoxTwin: Learning Elastoplastic Articulated Object Dynamics from Videos
Heng Zhang, Gehan Zheng, Kaifeng Zhang +6
Digital twins enable robots to anticipate and adapt to physical interactions, but existing models struggle with elastoplastic articulated objects (EAOs) that exhibit nonlinear elas…
Learning Physics-Guided Residual Dynamics for Deformable Object Simulation
Shivansh Patel, Kaifeng Zhang, Sanjay Pokkali +2
Simulating deformable objects is essential for a wide range of robotic manipulation applications, yet accurately predicting their dynamics remains challenging. We propose Physics-G…
Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities
Davood Soleymanzadeh, Ivan Lopez-Sanchez, Hao Su +3
State-of-the-art generalist manipulation policies have enabled the deployment of robotic manipulators in unstructured human environments. However, these frameworks struggle in clut…
ManipulationNet: An Infrastructure for Benchmarking Real-World Robot Manipulation with Physical Skill Challenges and Embodied Multimodal Reasoning
Yiting Chen, Kenneth Kimble, Edward H. Adelson +20
Dexterous manipulation enables robots to purposefully alter the physical world, transforming them from passive observers into active agents in unstructured environments. This capab…
Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos
Kaifeng Zhang, Baoyu Li, Kris Hauser +1
Modeling the dynamics of deformable objects is challenging due to their diverse physical properties and the difficulty of estimating states from limited visual information. We addr…
Localized Graph-Based Neural Dynamics Models for Terrain Manipulation
Chaoqi Liu, Yunzhu Li, Kris Hauser
Predictive models can be particularly helpful for robots to effectively manipulate terrains in construction sites and extraterrestrial surfaces. However, terrain state representati…
BaB-ND: Long-Horizon Motion Planning with Branch-and-Bound and Neural Dynamics
Keyi Shen, Jiangwei Yu, Jose Barreiros +2
Neural-network-based dynamics models learned from observational data have shown strong predictive capabilities for scene dynamics in robotic manipulation tasks. However, their inhe…
Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling
Mingtong Zhang, Kaifeng Zhang, Yunzhu Li
Videos of robots interacting with objects encode rich information about the objects' dynamics. However, existing video prediction approaches typically do not explicitly account for…
AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation
Kaifeng Zhang, Baoyu Li, Kris Hauser +1
Predictive models are a crucial component of many robotic systems. Yet, constructing accurate predictive models for a variety of deformable objects, especially those with unknown p…