Coaching robots with biosignals based on human affective social behaviors

We introduce a novel paradigm of social interaction between humans and robots, which is a style of coaching humanoid robots through interaction with a human instructor, who provides reinforcement via affective/social behaviors and biological signals. In particular facial Electromyography (EMG) to capture affective human response by using a personal wearable device is used as guidance or feedback to shape robot behavior. Through real-time pattern classification, facial expressions can be identified from them and interpreted as positive and negative responses from a human. We also developed a behavior-based architecture for testing this approach in the context of complex reactive robot behaviors.