SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy
For robotic assembly, this provides a unified model for search and insertion, improving robustness to misalignments without model switching.
SI-Diff learns both search and high-precision insertion for peg-in-hole tasks using a single force-domain diffusion policy, extending tolerance to x-y misalignments from 2 mm to 5 mm compared to TacDiffusion, with strong zero-shot transfer to unseen shapes.
Contact-rich assembly is fundamental in robotics but poses significant challenges due to uncertainties in relative poses, such as misalignments and small clearances in peg-in-hole tasks. Existing approaches typically address search and high-precision insertion separately, because these tasks involve distinct action patterns. However, supporting both tasks within a single model, without switching models or weights, is desirable for intelligent assembly systems. In this work, we propose SI-Diff, a framework that learns both search and high-precision insertion through a force-domain diffusion policy. To this end, we introduce a new mode-conditioning mechanism that enables the policy to capture distinct action behaviors under a single framework. Moreover, we develop a new search teacher policy that can generate diverse trajectories. By training on successful and efficient demonstrations provided by the teacher policy, the model learns the mapping from tactile and end-effector velocity observations to effective action behaviors. We conduct thorough experiments to show that SI-Diff extends the tolerance to x-y misalignments from 2 mm to 5 mm compared to the state-of-the-art baseline, TacDiffusion, while also demonstrating strong zero-shot transferability to unseen shapes.