Search Results for author:
Found 11 papers, 4 papers with code
DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects
However, for highly deformable objects it is challenging to align the output of a simulator with the behavior of real objects.
A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation
Deformable object manipulation remains a challenging task in robotics research.
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
This paper outlines BayesSimIG: a library that provides an implementation of BayesSim integrated with the recently released NVIDIA IsaacGym.
Sequential Topological Representations for Predictive Models of Deformable Objects
Deformable objects present a formidable challenge for robotic manipulation due to the lack of canonical low-dimensional representations and the difficulty of capturing, predicting, and controlling such objects.
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
Our second contribution is a unifying mathematical formulation for learning latent relations.
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
We propose a model and architecture for a sequential variational autoencoder that embeds the space of simulated trajectories into a lower-dimensional space of latent paths in an unsupervised way.
Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores.
Deep Kernels for Optimizing Locomotion Controllers
First, we demonstrate improvement in sample efficiency when optimizing a 5-dimensional controller on the ATRIAS robot hardware.
Unlocking the Potential of Simulators: Design with RL in Mind
We design a simulator for solving a pivoting task (of interest in Robotics) and demonstrate that even a simple simulator designed with RL in mind outperforms high-fidelity simulators when it comes to learning a policy that is to be deployed on a real robotic system.
Reinforcement Learning for Pivoting Task
In this work we propose an approach to learn a robust policy for solving the pivoting task.
Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion
We develop a distance metric for bipedal locomotion that enhances the sample-efficiency of Bayesian Optimization and use it to train a 16 dimensional neuromuscular model for planar walking.
