Learning to Recognize and Grasp Objects
Machine Learning - Special issue on learning in autonomous robots
Learning to Recognize and Grasp Objects
Autonomous Robots
The Chin Pinch: A Case Study in Skill Learning on a Legged Robot
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A scheme for learning to grasp objects using visual information is presented. A system is considered that coordinates a parallel-jaw gripper (hand) and a camera (eye). Given an object, and considering its geometry, the system chooses grasping points, and performs the grasp. The system learns while performing grasping trials. For each grasp we store location parameters that code the locations of the grasping points, quality parameters that are relevant features for the assessment of grasp quality, and the grade. We learn two separate subproblems: (1) to choose the grasping points, and (2) to predict the quality of a given grasp. The location parameters are used to locate grasping points on new target objects. We consider a function from the quality parameters to the grade, learn the function from examples, and later use it to estimate grasp quality. In this way grasp quality for novel situations can be generalized and estimated.