Human emotions are expressed through multiple modalities, including verbal and non-verbal information. Moreover, the affective states of human users can be the indicator for the level of engagement and successful interaction, suitable for the robot to use as a rewarding factor to optimize robotic behaviors through interaction. This study demonstrates a multimodal human-robot interaction (HRI) framework with reinforcement learning to enhance the robotic interaction policy and personalize emotional interaction for a human user. The goal is to apply this framework in social scenarios that can let the robots generate a more natural and engaging HRI framework.
翻译:人类情感通过多种方式表达,包括口头和非口头信息;此外,人类用户的情感状态可以作为参与和成功互动程度的指标,适合机器人作为通过互动优化机器人行为的有益因素;本研究报告展示了多式人类机器人互动框架,强化学习,加强机器人互动政策,使人类用户的情感互动个人化;目标是在社会情景中应用这一框架,使机器人能够产生更自然、更有吸引力的HRI框架。