SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy

• URL: http://arxiv.org/abs/2605.12247v1
• Date: Tue, 12 May 2026 15:14:51 GMT
• Title: SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy
• Abstract summary: We propose SI-Diff, a framework that learns both search and high-precision insertion through a force-domain diffusion policy.<n>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.
• Score: 5.439341986309672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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