GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation

TL;DR

GarmentLab introduces a comprehensive benchmark and realistic simulation platform for garment manipulation, addressing current limitations in task diversity and simulation fidelity to advance robotics research.

Contribution

The paper presents GarmentLab, a new benchmark with diverse tasks, multiple simulation methods, and real-world testing to improve garment manipulation research.

Findings

Current algorithms struggle with generalization in garment tasks.

Simulation methods like FEM and PBD enhance realism.

Benchmark facilitates progress in vision, reinforcement, and imitation learning.

Abstract

Manipulating garments and fabrics has long been a critical endeavor in the development of home-assistant robots. However, due to complex dynamics and topological structures, garment manipulations pose significant challenges. Recent successes in reinforcement learning and vision-based methods offer promising avenues for learning garment manipulation. Nevertheless, these approaches are severely constrained by current benchmarks, which offer limited diversity of tasks and unrealistic simulation behavior. Therefore, we present GarmentLab, a content-rich benchmark and realistic simulation designed for deformable object and garment manipulation. Our benchmark encompasses a diverse range of garment types, robotic systems and manipulators. The abundant tasks in the benchmark further explores of the interactions between garments, deformable objects, rigid bodies, fluids, and human body. Moreover, by incorporating multiple simulation methods such as FEM and PBD, along with our proposed sim-to-real algorithms and real-world benchmark, we aim to significantly narrow the sim-to-real gap. We evaluate state-of-the-art vision methods, reinforcement learning, and imitation learning approaches on these tasks, highlighting the challenges faced by current algorithms, notably their limited generalization capabilities. Our proposed open-source environments and comprehensive analysis show promising boost to future research in garment manipulation by unlocking the full potential of these methods. We guarantee that we will open-source our code as soon as possible. You can watch the videos in supplementary files to learn more about the details of our work. Our project page is available at: https://garmentlab.github.io/