4 accepted papers
Adaptive learning is critical to helping robots personalize their interactions with people, particularly when considering skills needed by socially assistive robots, such as persuasion. In this letter, we propose a novel, hybrid hierarchical learning architecture for use in social human-robot intera
As robots work in increasingly collaborative settings with humans, scenarios will arise where robots need to successfully request favors to be effective. Communication directness and familiarity have been shown to be important factors to persuasion. However, these two critical factors have not yet b
This letter presents an exploratory social human- robot interaction (HRI) study that investigates and compares the persuasive effectiveness of robots attempting to influence a user with different behavior strategies. Ten multimodal persuasive strategies were uniquely designed based on compliance gai
For robots to become collaborative assistants, they need to be capable of naturally interacting with users in real environments. They also need to be able to learn new skills from non-expert users. In this letter, we present a novel parallel hidden Markov model (PaHMM) architecture for learning from