Manifold Generative Annealing for Contact Constraint-aware Impedance Control with Reinforcement Learning Prior
Manifold Generative Annealing (MGA) is a framework that combines learned reinforcement learning policies with model-based optimization to control robot motion and impedance during contact tasks. It generates structured interaction proposals, refines them using geometric constraints to ensure physical realizability, and adapts online when contact conditions change. This allows for safer and more robust performance in complex scenarios like peg insertion.