Semantic State Estimation in Cloth Manipulation Tasks
This work addresses the challenge of understanding deformable object manipulations for robotics, though it is incremental as it builds on existing methods with new data.
The paper tackles the problem of semantic state estimation in cloth manipulation by introducing a new large-scale annotated RGB image dataset and benchmarking baseline deep networks, achieving competitive performance on the new benchmark.
Understanding of deformable object manipulations such as textiles is a challenge due to the complexity and high dimensionality of the problem. Particularly, the lack of a generic representation of semantic states (e.g., \textit{crumpled}, \textit{diagonally folded}) during a continuous manipulation process introduces an obstacle to identify the manipulation type. In this paper, we aim to solve the problem of semantic state estimation in cloth manipulation tasks. For this purpose, we introduce a new large-scale fully-annotated RGB image dataset showing various human demonstrations of different complicated cloth manipulations. We provide a set of baseline deep networks and benchmark them on the problem of semantic state estimation using our proposed dataset. Furthermore, we investigate the scalability of our semantic state estimation framework in robot monitoring tasks of long and complex cloth manipulations.