Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization

19 Feb 2020 • Manuel Kaspar • Juan David Munoz Osorio • Jürgen Bock

In this work we show how to use the Operational Space Control framework (OSC) under joint and cartesian constraints for reinforcement learning in cartesian space. Our method is therefore able to learn fast and with adjustable degrees of freedom, while we are able to transfer policies without additional dynamics randomizations on a KUKA LBR iiwa peg in-hole task... (read more)

PDF Abstract

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
🤖 No Methods Found	Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet

Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization

Code

Add Remove Mark official

Tasks

Add Remove

Results from the Paper

Edit

Methods used in the Paper

Add Remove

Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Results from the Paper Edit

Methods used in the Paper Edit Add Remove

Code

Add Remove Mark official

Tasks

Add Remove

Results from the Paper

Edit

Methods used in the Paper

Add Remove