ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (9): 1854-1863.doi: 10.7544/issn1000-1239.2020.20200254

所属专题: 2020边缘计算专题

• 网络技术 • 上一篇    下一篇

机器人4.0: 边缘计算支撑下的持续学习和时空智能

王志刚1,王海涛2,佘琪1,史雪松1,张益民1   

  1. 1(英特尔中国研究院 北京 100080);2(英特尔亚太研发有限公司 上海 200241) (zhi.gang.wang@intel.com)
  • 出版日期: 2020-09-01

Robot 4.0: Continual Learning and Spatial-Temporal Intelligence Through Edge

Wang Zhigang1, Wang Haitao2, She Qi1, Shi Xuesong1, Zhang Yimin1   

  1. 1(Intel Labs China, Beijing 100080);2(Intel Asia Pacific Research and Development Co., Ltd., Shanghai 200241)
  • Online: 2020-09-01

摘要: 随着全球机器人市场规模的不断扩大,机器人技术正在从机器人3.0时代迈向机器人4.0时代.这除了要求机器人具备感知能力,实现智能协作外,还要求其具有理解和决策的能力,最终实现自主的服务.尽管人工智能研究已经借深度学习技术取得突破性进展,但要实现机器人如人类一样做出决策,依然是非常具有挑战的目标,还有许多难点亟待解决.对有望解决这些问题的3项关键技术——持续学习、时空智能和边缘计算进行了初步探讨:通过持续学习,机器人能够将旧任务的知识快速迁移到新的任务中,并解决灾难性遗忘问题;通过时空智能让机器人对周围的环境建立起从高层到底层的表示,并像人一样从不同的粒度上分享和解决问题;最后充分利用边缘计算提供更高性价比的服务,把各种智能和知识很好地组合起来,实现规模化部署.

关键词: 机器人, 机器人4.0, 持续学习, 时空智能, 边缘计算

Abstract: With the expansion of the global robot market, robotics is moving from the robot 3.0 era to the robot 4.0 era. In robot 4.0 era, robots should not only have the capability of perception and collaboration, but also have the capability of understanding the environment and making decisions by themselves just like human being. Then they can provide service to people autonomously. Although there have been many breakthroughs in deep learning, it is still a very challenging goal to make robots understand environment and make decisions like humans being. This paper explores three key technologies that are expected to solve these problems: continual learning, spatial-temporal intelligence, and edge computing. Continual learning enables robots to migrate the knowledge of old tasks to the knowledge of new tasks quickly without catastrophic forgotten problems; spatial-temporal intelligence enables robots to establish a bottom-up knowledge representation of the environment and to share and solve problems at different levels. Through edge computing, robots can get more cost-effective computation resource and integrate a variety of intelligence and knowledge easily. It is very useful for the large-scale deployment. These technologies are on the rise, and this paper is just a preliminary analysis.

Key words: robot, robot 4.0, continual learning, spatial-temporal intelligence, edge computing

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