Mitigating the Uncanny Valley Effect in Hyper-Realistic Robots: A Student-Centered Study on LLM-Driven Conversations
It addresses the problem of user acceptance for hyper-realistic social robots, offering incremental insights for human-robot interaction design.
This study tackled the uncanny valley effect in hyper-realistic robots by testing if LLM-driven conversations can reduce eeriness, finding that interactions with a robot named Nadine significantly lowered feelings of uncanniness and improved conversational quality among 80 participants.
The uncanny valley effect poses a significant challenge in the development and acceptance of hyper-realistic social robots. This study investigates whether advanced conversational capabilities powered by large language models (LLMs) can mitigate this effect in highly anthropomorphic robots. We conducted a user study with 80 participants interacting with Nadine, a hyper-realistic humanoid robot equipped with LLM-driven communication skills. Through pre- and post-interaction surveys, we assessed changes in perceptions of uncanniness, conversational quality, and overall user experience. Our findings reveal that LLM-enhanced interactions significantly reduce feelings of eeriness while fostering more natural and engaging conversations. Additionally, we identify key factors influencing user acceptance, including conversational naturalness, human-likeness, and interestingness. Based on these insights, we propose design recommendations to enhance the appeal and acceptability of hyper-realistic robots in social contexts. This research contributes to the growing field of human-robot interaction by offering empirical evidence on the potential of LLMs to bridge the uncanny valley, with implications for the future development of social robots.