Learning to Arbitrate Human and Robot Control using Disagreement between Sub-Policies
Yoojin Oh,Marc Toussaint,Jim Mainprice,Yoojin Oh,Marc Toussaint,Jim Mainprice
In the context of teleoperation, arbitration refers to deciding how to blend between human and autonomous robot commands. We present a reinforcement learning solution that learns an optimal arbitration strategy that allocates more control authority to the human when the robot comes across a decision point in the task. A decision point is where the robot encounters multiple options (sub-policies), ...
