论文标题
人类机器人相互作用期间对人类模型的安全有效探索
Safe and Efficient Exploration of Human Models During Human-Robot Interaction
论文作者
论文摘要
许多协作的人类机器人任务要求机器人保持安全并在人类周围有效地工作。由于机器人只能在人类的模型上保持安全，因此我们希望机器人能够学习人类的好模型，以便安全有效地采取行动。本文研究了使机器人能够安全探索人类机器人系统的空间以改善机器人人类模型的空间的方法，从而使机器人可以访问更大的状态空间并与人类更好地工作。特别是，我们在基于能量功能的安全控制框架下引入了主动探索，研究不同的主动勘探策略的效果，并最终分析了安全主动探索对分析和神经网络人类模型的影响。
Many collaborative human-robot tasks require the robot to stay safe and work efficiently around humans. Since the robot can only stay safe with respect to its own model of the human, we want the robot to learn a good model of the human in order to act both safely and efficiently. This paper studies methods that enable a robot to safely explore the space of a human-robot system to improve the robot's model of the human, which will consequently allow the robot to access a larger state space and better work with the human. In particular, we introduce active exploration under the framework of energy-function based safe control, investigate the effect of different active exploration strategies, and finally analyze the effect of safe active exploration on both analytical and neural network human models.
