Construction of a 3D Object Recognition and Manipulation Database from Grasp Demonstrations

Object recognition and manipulation are critical for enabling robots to operate within a household environment. There are many grasp planners that can estimate grasps based on object shape, but these approaches often perform poorly because they miss key information about non-visual object characteristics, such as weight distribution, fragility of materials, and usability characteristics. Object model databases can account for this information, but existing methods for constructing 3D object recognition databases are time and resource intensive, often requiring specialized equipment, and are therefore di cult to apply to robots in the eld. We present an easy-to-use system for constructing object models for 3D object recognition and manipulation made possible by advances in web robotics. The database consists of point clouds generated using a novel iterative point cloud registration algorithm, which includes the encoding of manipulation data and usability characteristics. The system requires no additional equipment other than the robot itself, and non-expert users can demonstrate grasps through an intuitive web interface with virtually no training required. We validate the system with data collected from both a crowdsourcing user study and a set of grasps demonstrated by an expert user. We show that the crowdsourced grasps can produce successful autonomous grasps, and furthermore the demonstration approach outperforms purely vision-based grasp planning approaches for a wide variety of object classes.