. Policies for opening a cabinet drawer and swing-peg-in-hole tasks trained by alternatively performing reinforcement learning with multiple agents in simulation and updating simulation parameter distribution using a few real world policy executions. Abstract-We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the randomization of simulations, we adapt the simulation parameter distribution using a few real world roll-outs interleaved with policy training. 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. We show that policies trained with our method are able to reliably transfer to different robots in two real world tasks: swing-peg-in-hole and opening a cabinet drawer. The video of our experiments can be found at https: //sites.google.com/view/simopt.
Lastly, interleaving policy roll outs between simulation and reality has also proven to work well on swing-peg-in-hole and opening a cabinet drawer tasks REF .
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
2019 International Conference on Robotics and Automation (ICRA)
2019 International Conference on Robotics and Automation (ICRA)
Engineering Computer Science