MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations

no code implementations • 26 Oct 2023 • Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, Dieter Fox

Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents.

Imitation Learning

DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality

2 code implementations • 25 Oct 2022 • Ankur Handa, Arthur Allshire, Viktor Makoviychuk, Aleksei Petrenko, Ritvik Singh, Jingzhou Liu, Denys Makoviichuk, Karl Van Wyk, Alexander Zhurkevich, Balakumar Sundaralingam, Yashraj Narang, Jean-Francois Lafleche, Dieter Fox, Gavriel State

Our policies are trained to adapt to a wide range of conditions in simulation.

Reinforcement Learning (RL)

Factory: Fast Contact for Robotic Assembly

1 code implementation • 7 May 2022 • Yashraj Narang, Kier Storey, Iretiayo Akinola, Miles Macklin, Philipp Reist, Lukasz Wawrzyniak, Yunrong Guo, Adam Moravanszky, Gavriel State, Michelle Lu, Ankur Handa, Dieter Fox

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

no code implementations • ICLR 2022 • Jie Xu, Viktor Makoviychuk, Yashraj Narang, Fabio Ramos, Wojciech Matusik, Animesh Garg, Miles Macklin

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

no code implementations • 19 Mar 2022 • Eric Heiden, Miles Macklin, Yashraj Narang, Dieter Fox, Animesh Garg, Fabio Ramos

In this work, we present DiSECt: the first differentiable simulator for cutting soft materials.

Bayesian Inference