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Scaling Face Interaction Graph Networks to Real World Scenes
Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design.
Learning 3D Particle-based Simulators from RGB-D Videos
Realistic simulation is critical for applications ranging from robotics to animation.
Learning rigid dynamics with face interaction graph networks
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions.
Physical Design using Differentiable Learned Simulators
In our fluid manipulation tasks, the resulting designs outperformed those found by sampling-based optimization techniques.
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates
This distribution can then be used as a policy certificate in downstream applications.
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