Paper ID: 2409.15159 • Published Sep 23, 2024
DRAPER: Towards a Robust Robot Deployment and Reliable Evaluation for Quasi-Static Pick-and-Place Cloth-Shaping Neural Controllers
Halid Abdulrahim Kadi, Jose Alex Chandy, Luis Figueredo, Kasim Terzić, Praminda Caleb-Solly
TL;DR
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Comparing robotic cloth-manipulation systems in a real-world setup is
challenging. The fidelity gap between simulation-trained cloth neural
controllers and real-world operation hinders the reliable deployment of these
methods in physical trials. Inconsistent experimental setups and hardware
limitations among different approaches obstruct objective evaluations. This
study demonstrates a reliable real-world comparison of different
simulation-trained neural controllers on both flattening and folding tasks with
different types of fabrics varying in material, size, and colour. We introduce
the DRAPER framework to enable this comprehensive study, which reliably
reflects the true capabilities of these neural controllers. It specifically
addresses real-world grasping errors, such as misgrasping and multilayer
grasping, through real-world adaptations of the simulation environment to
provide data trajectories that closely reflect real-world grasping scenarios.
It also employs a special set of vision processing techniques to close the
simulation-to-reality gap in the perception. Furthermore, it achieves robust
grasping by adopting a tweezer-extended gripper and a grasping procedure. We
demonstrate DRAPER's generalisability across different deep-learning methods
and robotic platforms, offering valuable insights to the cloth manipulation
research community.