"Please Be Nice": Robot Responses to User Bullying - Measuring Performance Across Aggression Levels
Yiming Luo, Shihao Liu, Di Wu, Hao Wang, Yushan Pan
Abstract
As robots become integral to public services, addressing harmful user behaviors like bullying is crucial. Existing research often overlooks the gradual nature of human bullying. This study fills this gap by exploring how robots can counter bullying through optimized responses. Using a simulated human-robot interaction study, we manipulated robot response behaviors and styles across escalating bullying severity. Results show that empathetic verbal responses promptly reduce users’ bullying tendencies by eliciting remorse and redirecting attention to social awareness. However, users’ underlying dispositions may override these reflexive reactions, emphasizing the need for a holistic understanding. In conclusion, a comprehensive approach is essential, involving immediate reaction optimization, emotional state assessment, and ongoing behavioral adjustment through empathetic dialogue. By implementing such strategies, we can transform human-robot relationships from potential bullying situations to harmonious interactions. This study provides an empirical foundation for response protocols that discourage bullying and enhance mutual understanding.
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