Shir Kozlovsky

Reinforcement Learning of Impedance Policies for Peg-in-Hole Tasks: Role of Asymmetric Matrices

Robotic manipulators are playing an increasing role in a wide range of industries. However, their application to assembly tasks is hampered by the need for precise control over the environment and for task-specific coding. Cartesian impedance control is a well-established method for interacting with