Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly

Understand

Precise robotic manipulation skills are desirable in many industrial settings, reinforcement learning (RL) methods hold the promise of acquiring these skills autonomously.

• In this paper, we explicitly consider incorporating operational space force/torque information into reinforcement learning; this is motivated by humans heuristically mapping perceived forces to control actions, which results in completing high-precision tasks in a fairly easy manner.
• Our approach combines RL with force/torque information by incorporating a proper operational space force controller; where we also exploit different ablations on processing this information.
• Moreover, we propose a neural network architecture that generalizes to reasonable variations of the environment.