  The study addresses the foundational and challenging task of peg-in-hole
assembly in robotics, where misalignments caused by sensor inaccuracies and
mechanical errors often result in insertion failures or jamming. This research
introduces PolyFit, representing a paradigm shift by transitioning from a
reinforcement learning approach to a supervised learning methodology. PolyFit
is a Force/Torque (F/T)-based supervised learning framework designed for 5-DoF
peg-in-hole assembly. It utilizes F/T data for accurate extrinsic pose
estimation and adjusts the peg pose to rectify misalignments. Extensive
training in a simulated environment involves a dataset encompassing a diverse
range of peg-hole shapes, extrinsic poses, and their corresponding contact F/T
readings. To enhance extrinsic pose estimation, a multi-point contact strategy
is integrated into the model input, recognizing that identical F/T readings can
indicate different poses. The study proposes a sim-to-real adaptation method
for real-world application, using a sim-real paired dataset to enable effective
generalization to complex and unseen polygon shapes. PolyFit achieves
impressive peg-in-hole success rates of 97.3% and 96.3% for seen and unseen
shapes in simulations, respectively. Real-world evaluations further demonstrate
substantial success rates of 86.7% and 85.0%, highlighting the robustness and
adaptability of the proposed method.
