Search Results for author: Erwin Coumans

Found 11 papers, 6 papers with code

Inferring Articulated Rigid Body Dynamics from RGBD Video

1 code implementation20 Mar 2022 Eric Heiden, Ziang Liu, Vibhav Vineet, Erwin Coumans, Gaurav S. Sukhatme

Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain.

Fast and Efficient Locomotion via Learned Gait Transitions

1 code implementation9 Apr 2021 Yuxiang Yang, Tingnan Zhang, Erwin Coumans, Jie Tan, Byron Boots

We focus on the problem of developing energy efficient controllers for quadrupedal robots.

Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks

no code implementations6 Dec 2020 Daniel Seita, Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, Ken Goldberg, Andy Zeng

Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag".

NeuralSim: Augmenting Differentiable Simulators with Neural Networks

2 code implementations9 Nov 2020 Eric Heiden, David Millard, Erwin Coumans, Yizhou Sheng, Gaurav S. Sukhatme

Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation parameters that best fit the observed sensor readings.


Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap

1 code implementation12 Jul 2020 Eric Heiden, David Millard, Erwin Coumans, Gaurav S. Sukhatme

We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the computation.

Learning Agile Robotic Locomotion Skills by Imitating Animals

no code implementations2 Apr 2020 Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Lee, Jie Tan, Sergey Levine

In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.

Domain Adaptation Imitation Learning +1

Policies Modulating Trajectory Generators

2 code implementations7 Oct 2019 Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke

We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller.

Learning Fast Adaptation with Meta Strategy Optimization

1 code implementation28 Sep 2019 Wenhao Yu, Jie Tan, Yunfei Bai, Erwin Coumans, Sehoon Ha

The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases.

Legged Robots Meta-Learning

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