Handling crowdsourced data using state space discretization for robot learning and synthesizing physical skills
Handling crowdsourced data using state space discretization for robot learning and synthesizing physical skills
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DOI：
10.1007/s41315-020-00152-1
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发表时间：
2020-11
影响因子：
1.7
通讯作者：
Leidi Zhao;Lu Lu-Lu;Cong Wang
中科院分区：
文献类型：
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作者：
Leidi Zhao;Lu Lu-Lu;Cong Wang
智能身体技能是机器人与现实世界互动所需的基本要素。与单一情况下从单个来源学习不同，长期从众包导师那里持续学习机器人提供了一条实现无处不在的机器人物理智能的实用途径。当机器人的智能还不足以自主行动时，导师可以是遥控机器人的人类司机。从一组遥操作人员那里可以不断地获得大量的感知运动数据，并通过机器学习来处理，不断地产生和提高机器人的自主物理技能。本文提出了一种利用状态空间离散化的学习方法，可持续地管理不断收集的数据，并综合自主机器人的技能。提出了两种状态空间离散化方法。对它们的优点和局限性进行了分析和比较。对两个物体操作挑战进行了仿真和物理测试，以检验所提出的学习方法。验证了处理系统不确定性、可持续地管理高维状态空间以及合成新技能或仅部分演示的新技能的能力。这项工作有望为生产先进的机器人物理智能提供长期和大规模的措施。
Intelligent physical skills are a fundamental element needed by robots to interact with the real world. Instead of learning from individual sources in single cases, continuous robot learning from crowdsourced mentors over long terms provides a practical path towards realizing ubiquitous robot physical intelligence. The mentors can be human drivers that teleoperate robots when their intelligence is not yet enough for acting autonomously. A large amount of sensorimotor data can be obtained constantly from a group of teleoperators, and processed by machine learning to continuously generate and improve the autonomous physical skills of robots. This paper presents a learning method that utilizes state space discretization to sustainably manage constantly collected data and synthesize autonomous robot skills. Two types of state space discretization have been proposed. Their advantages and limits are examined and compared. Simulation and physical tests of two object manipulation challenges are conducted to examine the proposed learning method. The capability of handling system uncertainty, sustainably managing high-dimensional state spaces, as well as synthesizing new skills or ones that have only been partly demonstrated are validated. The work is expected to provide a long-term and big-scale measure to produce advanced robot physical intelligence.