论文标题
与人类老师的TD-RL机器人系统达到透明度
Towards Transparency of TD-RL Robotic Systems with a Human Teacher
论文作者
论文摘要
对自主和灵活的HRI的高要求意味着在机器人控制中部署机器学习（ML）机制的必要性。实际上，使用ML技术（例如增强学习（RL））使机器人行为在学习过程中，而不是对观察用户透明的。在这项工作中，我们提出了一个情感模型，以提高人类机器人协作场景的RL任务的透明度。我们提出的架构通过一个能够接受人类反馈并根据学习过程表现出情感反应的情感模型来支持RL算法。该模型完全基于时间差（TD）误差。该体系结构在一个简单的设置中进行了孤立的实验室测试。结果强调，通过情感反应显示其内部状态足以使机器人透明其人类老师。人们还喜欢与响应式机器人互动，因为他们被用来通过情感和社会信号来理解自己的意图。
The high request for autonomous and flexible HRI implies the necessity of deploying Machine Learning (ML) mechanisms in the robot control. Indeed, the use of ML techniques, such as Reinforcement Learning (RL), makes the robot behaviour, during the learning process, not transparent to the observing user. In this work, we proposed an emotional model to improve the transparency in RL tasks for human-robot collaborative scenarios. The architecture we propose supports the RL algorithm with an emotional model able to both receive human feedback and exhibit emotional responses based on the learning process. The model is entirely based on the Temporal Difference (TD) error. The architecture was tested in an isolated laboratory with a simple setup. The results highlight that showing its internal state through an emotional response is enough to make a robot transparent to its human teacher. People also prefer to interact with a responsive robot because they are used to understand their intentions via emotions and social signals.
