While there has been significant progress to use simulated data to learn robotic manipulation of rigid objects, applying its success to deformable objects has been hindered by the lack of both deformable object models and realistic non-rigid body simulators. In this paper, we present Real Garment Benchmark (RGBench), a comprehensive benchmark for robotic manipulation of garments. It features a diverse set of over 6000 garment mesh models, a new high-performance simulator, and a comprehensive protocol to evaluate garment simulation quality with carefully measured real garment dynamics. Our experiments demonstrate that our simulator outperforms currently available cloth simulators by a large margin, reducing simulation error by 20% while maintaining a speed of 3 times faster. We will publicly release RGBench to accelerate future research in robotic garment manipulation. Website: https://rgbench.github.io/

Such problems have attracted a lot of interest for multiple reasons. From a theoretical (and even from a purely algorithmic) point of view, they constitute a clear and well-defined domain in which computational complexity issues can be analyzed and search/optimization algorithms can be put to work in challenging conditions. From a more practical point of view, there are many real-world problems which can be formalized as SSPs. Such problems are notably found in the area of computational biology, in which technological advances and the numerous initiatives are producing an unprecedented flood of data (Reichhardt, 1999) very much requiring the use of powerful computational tools to overcome the associated challenges (Meneses et al., 2005). Among such problems of interest from the perspective of SSPs we can cite discovering potential drug targets, creating diagnostic probes, designing primers, locating binding sites, or identifying consensus sequences just to name a few (Festa, 2007; Lanctot et al., 2003; Meneses et al., 2005).

Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables -- for instance, recovering brain networks from fMRI data. We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm. BOSS greedily searches over permutations of variables, using GSTs to construct and score DAGs from permutations. GSTs efficiently cache scores to eliminate redundant calculations. BOSS achieves state-of-the-art performance in accuracy and execution time, comparing favorably to a variety of combinatorial and gradient-based learning algorithms under a broad range of conditions. To demonstrate its practicality, we apply BOSS to two sets of resting-state fMRI data: simulated data with pseudo-empirical noise distributions derived from randomized empirical fMRI cortical signals and clinical data from 3T fMRI scans processed into cortical parcels. BOSS is available for use within the TETRAD project which includes Python and R wrappers.