Matthew Gombolay

schoolGeorgia Institute of Technologybar_chart#2062 Global Rank

Total Papers

HCI Rank

#2062

Research Areas

Years Active

Recent Publications

Enhancing Safety in Learning from Demonstration Algorithms via Control Barrier Function Shielding

Learning from Demonstration (LfD) is a powerful method for non-roboticists end-users to teach robots new tasks, enabling them to customize the robot behavior. However, modern LfD techniques do not explicitly synthesize safe robot behavior, which limits the deployability of these approaches in the real world. To enforc…

HRI '24CorrespondingBrain-Computer Interface (BCI) & Neurofeedback +2

Towards Balancing Preference and Performance through Adaptive Personalized Explainability

As robots and digital assistants are deployed in the real world, these agents must be able to communicate their decision-making criteria to build trust, improve human-robot teaming, and enable collaboration. While the field of explainable artificial intelligence (xAI) has made great strides in building a set of mechan…

HRI '23CorrespondingHuman-Robot Collaboration (HRC)

Impacts of Robot Learning on User Attitude and Behavior

With an aging population and a growing shortage of caregivers, the need for in-home robots is increasing. However, it is intractable for robots to have all functionalities pre-programmed prior to deployment. Instead, it is more realistic for robots to engage in supplemental, on-site learning about the user’s needs and…

HRI '23CorrespondingHuman-Robot Collaboration (HRC)

The Effect of Robot Skill Level and Communication in Rapid, Proximate Human-Robot Collaboration

As high-speed, agile robots become more commonplace, these robots will have the potential to better aid and collaborate with humans. However, due to the increased agility and functionality of these robots, close collaboration with humans can create safety concerns that alter team dynamics and degrade task performance.…

HRI '20CorrespondingBrain-Computer Interface (BCI) & Neurofeedback +2

Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation

Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the interactive hand-tuning of a reward function by RL experts. On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-ob…

HRI '20CorrespondingAgent Personality & Anthropomorphism +1

Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction

This paper examines how people’s trust and dependence on robot teammates providing decision support varies as a function of different attributes of the robot, such as perceived anthropomorphism, type of support provided by the robot, and its physical presence. We conduct a mixed-design user study with multiple robots…

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