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摘要: 结合人工智能技术和机器人技术，研究具备一定自主决策和学习能力的机器人操作技能学习系统，已逐渐成为机器人研究领域的重要分支.本文介绍了机器人操作技能学习的主要方法及最新的研究成果.依据对训练数据的使用方式将机器人操作技能学习方法分为基于强化学习的方法、基于示教学习的方法和基于小数据学习的方法，并基于此对近些年的研究成果进行了综述和分析，最后列举了机器人操作技能学习的未来发展方向.Abstract: Designing a robot manipulation skill learning system with autonomous reasoning and learning ability has gradually become an important branch of robotics research field in combination with artificial intelligence and robotics technology. In this paper, the main methods and the latest research results of robot manipulation skills learning methods are introduced. We divide the learning methods into three categories, namely reinforcement learning approach, demonstration learning approach, and few data learning approach. Achievements of the robot manipulation skills learning areas based on these methods are discussed thoroughly. Finally, the future research directions are listed.
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Key words:
- Robots /
- manipulation skills /
- reinforcement learning /
- imitation learning /
- few data learning
1) 本文责任编委 魏庆来 -
图 2 基于强化学习的操作技能学习示意图
Fig. 2 Illustration of manipulation skills learning method based on reinforcement learning
图 3 基于值函数强化学习的操作技能
Fig. 3 Manipulation skills based on value function of reinforcement learning
图 4 基于策略搜索强化学习的操作技能
Fig. 4 Manipulation skills based on policy search of reinforcement learning
表 1 机器人和其他应用中强化学习比较
Table 1 Comparison of reinforcement learning methods applied in robotics and other fields
项目 机器人应用 其他应用 状态、动作空间 均为高维、连续空间 大多为低维、离散空间 训练数据获取 真实环境:数据获取会损耗硬件, 有潜在危险, 成本高; 虚拟环境:数据获取方便 不损耗硬件不存在危险性 训练成本 仿真环境低, 真实环境高 低 主流方法 大多基于策略搜索 大多基于价值函数 其他方面 不确定性因素多, 训练过程受诸多条件约束, 学习过程需要人的参与 - 
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表 2 三类操作技能学习方法特点对比
Table 2 Comparison of three kinds of manipulation skills learning methods
对比项目 基于强化学习 基于示教学习 小数据学习 数据量 不需提供示教数据但需大量机器人与环境的交互数据 需提供较多示教数据 需大量数据面对新任务需少量数据 学习效率 低, 需不断试错 较高 高 学习成本 高 高 低
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