论文标题
基于模拟的机器人技术中的互动模仿学习
Interactive Imitation Learning in Robotics based on Simulations
论文作者
论文摘要
在各个行业中，对情报的转变是对智能和灵活产品的需求更大。在机器人技术领域，越来越多地应用基于学习的方法，目的是培训机器人，以学习通过数据处理复杂而不断变化的外部环境。在这种情况下，强化学习和模仿学习正在成为具有各自特征的研究热点。但是，两者在某些情况下有自己的局限性，例如增强学习的高数据获取成本。此外，模仿学习很难提供完美的演示。作为模仿学习的一个分支，互动模仿学习旨在通过示威者和机器人之间的互动将人类知识转移到代理商中，这减轻了教学的困难。本文在四个模拟方案中实现了IIL算法，并进行了广泛的实验，旨在提供有关行动空间和状态空间中IIL方法的详尽信息，并与RL方法进行比较。
The transformation towards intelligence in various industries is creating more demand for intelligent and flexible products. In the field of robotics, learning-based methods are increasingly being applied, with the purpose of training robots to learn to deal with complex and changing external environments through data. In this context, reinforcement learning and imitation learning are becoming research hotspots with their respective characteristics. However, the two have their own limitations in some cases, such as the high cost of data acquisition for reinforcement learning. Moreover, it is difficult for imitation learning to provide perfect demonstrations. As a branch of imitation learning, interactive imitation learning aims at transferring human knowledge to the agent through interactions between the demonstrator and the robot, which alleviates the difficulty of teaching. This thesis implements IIL algorithms in four simulation scenarios and conducts extensive experiments, aiming at providing exhaustive information about IIL methods both in action space and state space as well as comparison with RL methods.
