Open-end human–robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning
Open-end human–robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning
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DOI：
10.1163/1568553054255655
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发表时间：
2005-01
影响因子：
2
通讯作者：
T. Ogata;S. Sugano;J. Tani
中科院分区：
文献类型：
--
作者：
T. Ogata;S. Sugano;J. Tani
本文通过实验研究了人与机器人相互适应过程中产生的开放端交互作用。它的基本特征，增量学习，是用动态系统的方法来检验。我们的研究集中在专门开发的人形机器人Robovie的导航系统和7名人类受试者上，他们的眼睛被遮住了，使他们依赖机器人的方向。在机器人控制中，我们采用了常规的无递归连接的前馈神经网络（FFNN）和递归神经网络（RNN）。虽然在学习的早期，RNN和FFNN获得的性能都有所提高，但随着被试通过自己的学习改变操作，所有的性能都逐渐变得不稳定和失败。接下来，我们使用“巩固学习算法”作为大脑海马体的模型。在该方法中，RNN同时使用新数据和RNN的排练输出进行训练，以不破坏当前记忆的内容。所提出的方法使机器人即使在长时间（开放式）学习时也能提高性能。rnn的动态系统分析支持了这些差异，并表明协同方案是随着后续相变而动态发展的。
In this paper, we experimentally investigated the open-end interaction generated by the mutual adaptation between humans and robot. Its essential characteristic, incremental learning, is examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered, making them dependent on the robot for directions. We used the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN) for the robot control. Although the performances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own, all performances gradually became unstable and failed. Next, we used a 'consolidation-learning algorithm' as a model of the hippocampus in the brain. In this method, the RNN was trained by both new data and the rehearsal outputs of the RNN not to damage the contents of current memory. The proposed method enabled the robot to improve performance even when learning continued for a long time (open-end). The dynamical systems analysis of RNNs supports these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions.