As a human-robot interaction (HRI) researcher, I utilize a diverse range of data science tools and methodologies to bridge the gap between humans and robots. For example, I use advanced machine learning algorithms, including supervised and unsupervised learning, to analyze large datasets collected from human-robot interactions, thereby enabling the development of adaptive and responsive robotic systems. Techniques like natural language processing (NLP) are crucial for improving communication between humans and robots, while computer vision models enhance robots’ ability to interpret and respond to visual cues. Statistical analysis is used to analyze and present findings in an effective manner. My research also utilizes simulation platforms to test and refine robotic behaviors in virtual environments before deploying them in the real world. By integrating these data science tools and methodologies, my research enables the creation of intelligent, user-friendly robots that seamlessly interact with humans across diverse applications, ranging from healthcare to restaurants and beyond.
Lionel Robert
Professor, Robotics, College of Engineering
Professor, Information, School of Information
human-robot interaction, human-computer interaction, autonomous vehicles, human-AI interaction, security robots, robots in the workplace
COntact
Methodologies
Artificial Intelligence / Causal Inference / Computing / Machine Learning / Mathematical and Statistical Modeling / Statistics / Survey Methodology
Applications
Behavioral Science / Engineering / Healthcare Research / Informatics /