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Found 10 papers, 3 papers with code
SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing
Specifically, we first employ three text-conditioned 3D generative models to generate garment mesh, body shape and hair strands from the given text prompt.
UKAN: Unbound Kolmogorov-Arnold Network Accompanied with Accelerated Library
In this work, we present a GPU-accelerated library for the underlying components of Kolmogorov-Arnold Networks (KANs), along with an algorithm to eliminate bounded grids in KANs.
HandyPriors: Physically Consistent Perception of Hand-Object Interactions with Differentiable Priors
We demonstrate that HandyPriors attains comparable or superior results in the pose estimation task, and that the differentiable physics module can predict contact information for pose refinement.
Factory: Fast Contact for Robotic Assembly
We aim for Factory to open the doors to using simulation for robotic assembly, as well as many other contact-rich applications in robotics.
Accelerated Policy Learning with Parallel Differentiable Simulation
In this work we present a high-performance differentiable simulator and a new policy learning algorithm (SHAC) that can effectively leverage simulation gradients, even in the presence of non-smoothness.
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
In this work, we present DiSECt: the first differentiable simulator for cutting soft materials.
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU.
gradSim: Differentiable simulation for system identification and visuomotor control
We consider the problem of estimating an object's physical properties such as mass, friction, and elasticity directly from video sequences.
GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning
Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibitively large number of training samples for learning complex tasks.
Robotics
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world.
