TAPESIM: Efficient Simulation of Adhesive Tape Dispensing for Robotic Manipulation

2026-09-23

AI Summary

This study addresses the high computational cost and the inability of rigid rolls to release material in adhesive tape application simulations. To overcome these limitations, this work proposes a coupled model integrating a rigid body cluster with a variable-shape mandrel. By localizing deformation to the unwinding zone and incorporating rigid body clustering, local deformation modeling, and separable bonding techniques within a physics engine, the method enables efficient and reproducible robotic tape application simulation that supports dynamic material release and flexible reattachment. Experimental results demonstrate that the proposed approach accelerates physics step speed by 4.5 to 8.4 times, reduces errors by 23% to 29%, and achieves a peeling accuracy exceeding 72%.

Abstract

Applying adhesive tape to secure wire harnesses or seal packages requires robots to coordinate a flexible strip, a moving roll, and surfaces that attach and detach. Simulation could make these interactions repeatable for robot development and evaluation, but resolving every adhesive layer is expensive and can suppress roll motion at practical solver tolerances, while a permanently rigid roll cannot release material. We present TapeSim, a tape simulator that concentrates deformation near the unwinding region and along the released strip. We will release the source code. A rigid cluster represents most wound material, while an advancing deformable collar enables payout and leaves released tape flexible and reattachable. Optional releasable bonds simplify adhesive interfaces and reduce mean step times for smaller rolls. Controlled swing tests show improved roll rotation. At 32 turns, clustering gives 3.2-3.4x mean physics-step speedups at a fixed Newton tolerance and 4.5-8.4x for comparable roll motion. Across five real-motion Stick replays, the clustered variants reduce mean image-plane core-landmark error by 23-29% relative to the full-shell cohesive baseline. On 100 paired Peel cases, they improve balanced accuracy from 50% to 72.9-76.3%, with interface rankings varying across tasks. A teleoperated box-sealing sequence demonstrates attachment, dispensing, cutting, and sealing in a continuous workflow.

Problem

• adhesive tape simulation
• robotic manipulation
• tape dispensing
• physics-based simulation
• flexible body dynamics

Innovation

• adhesive tape simulation
• rigid clustering
• deformable collar
• releasable bonds
• robotic manipulation