Guided Exploration of Human Intentions for Human-Robot Interaction
Guided Exploration of Human Intentions for Human-Robot Interaction
复制标题
人机交互人类意图的引导探索
DOI：
10.1007/978-3-030-44051-0_53
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Wee Sun Lee
中科院分区：
文献类型：
--
作者：
Min Chen;David Hsu;Wee Sun Lee
机器人对人类意图的理解对于流畅的人机交互至关重要。然而，意图不能直接观察到，必须从行为中推断出来。我们学习一个以意图为潜在变量的适应性人类行为模型。然后，我们将人类行为模型嵌入到一个有原则的概率决策模型中，该模型使机器人能够（i）积极探索，以推断人类的意图，（ii）选择最大化其性能的行动。此外，机器人从人类专家的示范行动中学习，以进一步改善探索。初步的仿真实验表明，我们的方法，应用于自动驾驶，提高了效率和安全性的驾驶在常见的交互式驾驶场景。
Robot understanding of human intentions is essential for fluid human-robot interaction. Intentions, however, cannot be directly observed and must be inferred from behaviors. We learn a model of adaptive human behavior conditioned on the intention as a latent variable. We then embed the human behavior model into a principled probabilistic decision model, which enables the robot to (i) explore actively in order to infer human intentions and (ii) choose actions that maximize its performance. Furthermore, the robot learns from the demonstrated actions of human experts to further improve exploration. Preliminary experiments in simulation indicate that our approach, when applied to autonomous driving, improves the efficiency and safety of driving in common interactive driving scenarios.