  Fabric manipulation has applications in folding blankets, handling patient
clothing, and protecting items with covers. It is challenging for robots to
perform fabric manipulation since fabrics have infinite-dimensional
configuration spaces, complex dynamics, and may be in folded or crumpled
configurations with severe self-occlusions. Prior work on robotic fabric
manipulation relies either on heavily engineered setups or learning-based
approaches that create and train on robot-fabric interaction data. In this
paper, we propose GPT-Fabric for the canonical tasks of fabric folding and
smoothing, where GPT directly outputs an action informing a robot where to
grasp and pull a fabric. We perform extensive experiments in simulation to test
GPT-Fabric against prior state of the art methods for folding and smoothing. We
obtain comparable or better performance to most methods even without explicitly
training on a fabric-specific dataset (i.e., zero-shot manipulation).
Furthermore, we apply GPT-Fabric in physical experiments over 12 folding and 10
smoothing rollouts. Our results suggest that GPT-Fabric is a promising approach
for high-precision fabric manipulation tasks.
