To provide effective and enjoyable human-robot interaction, it is important for social robots to exhibit nonverbal behaviors, such as a handshake or a hug. However, the traditional approach of reproducing pre-coded motions allows users to easily predict the reaction of the robot, giving the impression that the robot is a machine rather than a real agent. Therefore, we propose a neural network architecture based on the Seq2Seq model that learns social behaviors from human-human interactions in an end-to-end manner. We adopted a generative adversarial network to prevent invalid pose sequences from occurring when generating long-term behavior. To verify the proposed method, experiments were performed using the humanoid robot Pepper in a simulated environment. Because it is difficult to determine success or failure in social behavior generation, we propose new metrics to calculate the difference between the generated behavior and the ground-truth behavior. We used these metrics to show how different network architectural choices affect the performance of behavior generation, and we compared the performance of learning multiple behaviors and that of learning a single behavior. We expect that our proposed method can be used not only with home service robots, but also for guide robots, delivery robots, educational robots, and virtual robots, enabling the users to enjoy and effectively interact with the robots.
翻译:为了提供有效和令人愉快的人类机器人互动,社会机器人必须展示非语言行为,如握手或拥抱。然而,传统复制预编码动作的方法让用户能够很容易地预测机器人的反应,给人的印象是机器人是机器而不是真正的代理人。因此,我们提议以Seq2Seq2Seq模型为基础的神经网络架构,该模型以最终到最后的方式学习人类互动的社会行为。我们采用了基因对抗网络,以防止产生长期行为时出现无效的构成序列。为了核实拟议方法,在模拟环境中使用人造机器人辣椒进行了实验。由于很难确定社会行为生成的成败,我们提出了新的指标来计算生成的行为与地心律行为之间的差异。我们用这些尺度来显示不同的网络结构选择如何影响行为生成的绩效,我们比较了学习多种行为的表现和学习单一行为的过程。我们期望,为了验证拟议的方法,在模拟环境中使用人造机器人,我们所提议的方法不仅能够有效地使用,而且能够使机器人和机器人互动。