Variable impedance control in operation-space is a promising approach to learning contact-rich manipulation behaviors. One of the main challenges with this approach is producing a manipulation behavior that ensures the safety of the arm and the environment. Such behavior is typically implemented via a reward function that penalizes unsafe actions (e.g. obstacle collision, joint limit extension), but that approach is not always effective and does not result in behaviors that can be reused in slightly different environments. We show how to combine Riemannian Motion Policies, a class of policies that dynamically generate motion in the presence of safety and collision constraints, with variable impedance operation-space control to learn safer contact-rich manipulation behaviors.
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