计算机科学
人工智能
机器人
主动学习(机器学习)
人机交互
机器人学
移动机器人
机器人控制
社交机器人
任务(项目管理)
控制(管理)
人工神经网络
作者
Oliver Kroemer,Renaud Detry,Justus Piater,Jan Peters
标识
DOI:10.1016/j.robot.2010.06.001
摘要
Grasping an object is a task that inherently needs to be treated in a hybrid fashion. The system must decide both where and how to grasp the object. While selecting where to grasp requires learning about the object as a whole, the execution only needs to reactively adapt to the context close to the grasp's location. We propose a hierarchical controller that reflects the structure of these two sub-problems, and attempts to learn solutions that work for both. A hybrid architecture is employed by the controller to make use of various machine learning methods that can cope with the large amount of uncertainty inherent to the task. The controller's upper level selects where to grasp the object using a reinforcement learner, while the lower level comprises an imitation learner and a vision-based reactive controller to determine appropriate grasping motions. The resulting system is able to quickly learn good grasps of a novel object in an unstructured environment, by executing smooth reaching motions and preshaping the hand depending on the object's geometry. The system was evaluated both in simulation and on a real robot.
科研通智能强力驱动
Strongly Powered by AbleSci AI