Mutual Alignment Transfer Learning

Understand

Training robots for operation in the real world is a complex, time consuming and potentially expensive task.

• Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress.
• While sample complexity can be reduced by training policies in simulation, such policies can perform sub-optimally on the real platform given imperfect calibration of model dynamics.
• We present an approach -- supplemental to fine tuning on the real robot -- to further benefit from parallel access to a simulator during training and reduce sample requirements on the real robot.