  Reinforcement Learning (RL) methods have been proven successful in solving
manipulation tasks autonomously. However, RL is still not widely adopted on
real robotic systems because working with real hardware entails additional
challenges, especially when using rigid position-controlled manipulators. These
challenges include the need for a robust controller to avoid undesired
behavior, that risk damaging the robot and its environment, and constant
supervision from a human operator. The main contributions of this work are,
first, we proposed a learning-based force control framework combining RL
techniques with traditional force control. Within said control scheme, we
implemented two different conventional approaches to achieve force control with
position-controlled robots; one is a modified parallel position/force control,
and the other is an admittance control. Secondly, we empirically study both
control schemes when used as the action space of the RL agent. Thirdly, we
developed a fail-safe mechanism for safely training an RL agent on manipulation
tasks using a real rigid robot manipulator. The proposed methods are validated
on simulation and a real robot, an UR3 e-series robotic arm.
