Arjun Singh

TL;DR: The Yale-CMU-Berkeley (YCB) Object and Model set is intended to be used for benchmarking in robotic grasping and manipulation research, and provides high-resolution RGBD scans, physical properties and geometric models of the objects for easy incorporation into manipulation and planning software platforms.

TL;DR: The Yale-Carnegie Mellon University-Berkeley object and model set is presented, intended to be used to facilitate benchmarking in robotic manipulation research and to enable the community of manipulation researchers to more easily compare approaches and continually evolve standardized benchmarking tests and metrics as the field matures.

TL;DR: A high-quality, large-scale dataset of 3D object instances, with accurate calibration information for every image, is presented, anticipating that “solving” this dataset will effectively remove many perception-related problems for mobile, sensing-based robots.

TL;DR: An image and model dataset of the real-life objects from the Yale-CMU-Berkeley Object Set, which is specifically designed for benchmarking in manipulation research, is presented.

TL;DR: The Yale-CMU-Berkeley Object and Model Set (YCB) as mentioned in this paper is a set of objects and models designed to cover a wide range of aspects of the manipulation problem, including objects of daily life with different shapes, sizes, textures, weight and rigidity.