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The study addresses the foundational and challenging task of peg-in-holeassembly in robotics, where misalignments caused by sensor inaccuracies andmechanical errors often result in insertion failures or jamming. This researchintroduces PolyFit, representing a paradigm shift by transitioning from areinforcement learning approach to a supervised learning methodology. PolyFitis a Force/Torque (F/T)-based supervised learning framework designed for 5-DoFpeg-in-hole assembly. It utilizes F/T data for accurate extrinsic poseestimation and adjusts the peg pose to rectify misalignments. Extensivetraining in a simulated environment involves a dataset encompassing a diverserange of peg-hole shapes, extrinsic poses, and their corresponding contact F/Treadings. To enhance extrinsic pose estimation, a multi-point contact strategyis integrated into the model input, recognizing that identical F/T readings canindicate different poses. The study proposes a sim-to-real adaptation methodfor real-world application, using a sim-real paired dataset to enable effectivegeneralization to complex and unseen polygon shapes. PolyFit achievesimpressive peg-in-hole success rates of 97.3% and 96.3% for seen and unseenshapes in simulations, respectively. Real-world evaluations further demonstratesubstantial success rates of 86.7% and 85.0%, highlighting the robustness andadaptability of the proposed method.

PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation