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As human-robot interactions become more social, a robot's personality plays an increasingly vital role in shaping user experience and its overall effectiveness. In this study, we examine the impact of three distinct robot personalities on user experiences during well-being exercises: a Baseline Personality that aligns with user expectations, a High Extraversion Personality, and a High Neuroticism Personality. These personalities were manifested through the robot's dialogue, which were generated using a large language model (LLM) guided by key behavioral characteristics from the Big 5 personality traits. In a between-subjects user study (N = 66), where each participant interacted with one distinct robot personality, we found that both the High Extraversion and High Neuroticism Robot Personalities significantly enhanced participants' emotional states (arousal, control, and valence). The High Extraversion Robot Personality was also rated as the most enjoyable to interact with. Additionally, evidence suggested that participants' personality traits moderated the effectiveness of specific robot personalities in eliciting positive outcomes from well-being exercises. Our findings highlight the potential benefits of designing robot personalities that deviate from users' expectations, thereby enriching human-robot interactions.
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Augmenting Simulation Data with Sensor Effects for Improved Domain Transfer
Simulation provides vast benefits for the field of robotics and Human-Robot Interaction (HRI). This study investigates how sensor effects seen in the real domain can be modeled in simulation and what role they play in effective Sim2Real domain transfer for learned perception models. The study considers introducing naive noise approaches such as additive Gaussian and salt and pepper noise as well as data-driven sensor effects models into simulation for representing Microsoft Kinect sensor capabilities and phenomena seen on real world systems. This study quantifies the benefit of multiple approaches to modeling sensor effects in simulation for Sim2Real domain transfer by their object classification improvements in the real domain. User studies are conducted to address hypotheses by training grounded language models in each of the sensor effects modeling cases and evaluated on the robot's interaction capabilities in the real domain. In addition to grounded language performance metrics, user study evaluation includes surveys on the human participant's assessment of the robot's capabilities in the real domain. Results from this pilot study show benefits to modeling sensor noise in simulation for Sim2Real domain transfer. This study also begins to explore the effects that such models have on human-robot interactions.
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- PAR ID:
- 10382789
- Date Published:
- Journal Name:
- Tenth International Workshop on Assistive Computer Vision and Robotics (ACVR) at ECCV
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
