Roll-Drop: accounting for observation noise with a single parameter

No Randomisation, the robot was trained and tested with Kp=15 and Kd=1

When trained is simulation without any form of randomisation to account for the sim-to-real gap the robot fails 3 times in a minute and a half.

Roll-Drop was adopted to account for observation noise, the robot was trained and tested with Kp=15 and Kd=1

When Roll-Drop is included we can run the controller indefinitely without failures, in the video on the left more than four minutes.

No Randomisation, the robot was trained in simulation with Kp=20 and Kd=1 and tested with Kp=15 and Kd=1.

The policy trained in simulation without any sim-to-real transfer technique is not able to account for the sim-to-real gap.

Roll-Drop was adopted to account for observation noise, the robot was trained in simulation with Kp=20 and Kd=1 and tested with Kp=15 and Kd=1.

The change in gains highlights a case of system uncertainty and demonstrates that Roll-Drop is capable of addressing sim-to-real gaps.