The Societal Impact of AI Decisions

Exploring how robot decision-making drives human behavior

Algorithms for Human-Aligned Decision Making

Designing algorithms that adapt AI behavior to human values

In this line of work, I develop algorithms that enable agents and robots to learn human values during collaboration. On the technical side, I draw from reinforcement learning, deep learning, inverse reinforcement learning, and large language models to build systems that can capture and reason about human values. On the applied side, I test these approaches in interactive settings with robots and AI systems, from assistive physical robots to multi-agent games and language-based collaborators.

Publications:

• Candon, K., Zhang, Q., Lew, A., Claure, H., Sarkar, C., & Vázquez, M. 2026. Learning human preferences over a human-robot collaboration based on explicit and implicit human feedback. ACM/IEEE International Conference on Human-Robot Interaction (HRI).
• Narcomey, A., Claure, H., & Vázquez, M. 2025. Robot Delivery of Actionable Counterfactual Neural Network Explanations: Results in Group Perception. IEEE International Conference on Robot and Human Interactive Communication (RO-MAN).
• Claure, H., Candon, K., & Vázquez, M. 2024. Dynamic Fairness Perceptions in Human-Robot Interaction. ACM Transactions on Human-Robot Interaction (THRI). Under Review.
• Claure, H., Chen, Y., Modi, J., Jung, M., & Nikolaidis, S. 2020. Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates. ACM/IEEE International Conference on Human-Robot Interaction (HRI).

Interactive Hardware Design

Building and Studying Hardware Designs on Human Experiences

In these projects I developed different hardware and software platforms as well as explored different aspects of human interaction with technology. For instance, the project image below was a collaboration with Dr. Robert Shepherd's Organic Robotics Laboratory (ORL) at Cornell University and NVIDIA. The goal was to develop a novel type of virtual reality controller sleeve that is capable of providing haptic feedback. We developed and tested a 12 DOF fluidically pressurized soft actuator for persistent and kinesthetic haptic sensations. We tested the controller through a user experience test after the development of a software interface with NVIDIA's game VR Funhhouse. Results demonstrated which haptic stimulations from the controller were more recognizable by human participants as well as how the controller sleeve influenced the game experience.

Publications:

• Lee, W., Sakashita, M., Ricci, A., Claure, H., Jung, M., & Guimbretière, F. 2021. Interactive Vignettes: Enabling Large-Scale Interactive HRI Research. 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN).
• Claure, H., Khojasteh, N., Tennet, H., & Jung, M. 2020. Using Expectancy Violations Theory to Understand Robot Touch Interpretation. Companion of the ACM/IEEE International Conference on Human-Robot Interaction.
• Claure, H.*, Barrieros, J.*, Peele, B.*, & Shepherd, R. 2018. Fluidic Elastomer Actuators for Haptic Interactions in Virtual Reality. IEEE Robotics and Automation Letters (RA-L). (*Authors contributed equally.)
• Shen, S., Tennent, H., Claure, H., & Jung, M. 2018. My Telepresence, My Culture? An Intercultural Investigation of Telepresence Robot Operators’ Interpersonal Distance Behaviors. Conference on Human Factors in Computing Systems (CHI).
• Stogin, B., Gockowski, L., Feldstein, H., Claure, H., Wang, J., & Wong, T. 2018. Free-standing liquid membranes as unusual particle separators. Science Advances.
• Claure, H., & Jung, M. 2018. Understanding Social Dynamics in Robot-Human Handovers Through the Lens of Expectancy Violations Theory. Companion of the ACM/IEEE Conference on Human-Robot Interaction (HRI).