Search Results for author:
Found 8 papers, 0 papers with code
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.
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
But human beings remain distinctive in their capacity for flexible, creative tool use -- using objects in new ways to act on the world, achieve a goal, or solve a problem.
Few-Shot Bayesian Imitation Learning with Logical Program Policies
We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples.
Infinite Mixture Prototypes for Few-Shot Learning
We propose infinite mixture prototypes to adaptively represent both simple and complex data distributions for few-shot learning.
Relational inductive bias for physical construction in humans and machines
While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks.
