Learning Active ForceCTorque Based Policy for Sub-mm Localization of Unseen Holes

Hole localization is crucial in the peg-in-hole process. Our goal is to enable robots to operate effectively in contact-rich environments with tight tolerances, and adapt to new tasks involving unseen peg-hole pairs. Most existing "black-box" methods train a policy that performs the task directly from perceptual inputs, which requires extensive real-world interactions for task adaptation. Departing from this direct mapping paradigm, our work propose to formulate the task as a force matching and localization problem, where the objective is to establish correspondences between current and template force-torque observation maps for localization purpose. The formulation enables the design of a decoupled map-locator-policy framework, offering improved success rates, efficiency, and augmented generalization capabilities, surpassing current state-of-the-art methods. Experiments demonstrate the effectiveness of the proposed method, achieving a 90% success rate across 12 unseen 3-D models and a variety of unseen tight workpieces. Within a mere 5-min adaption process, the performance can be further improved by more than 95%.