Interview with Haimin Hu: Sport-theoretic integration of security, interplay and studying for human-centered autonomy

On this interview collection, we’re assembly a few of the AAAI/SIGAI Doctoral Consortium individuals to seek out out extra about their analysis. On this newest interview, Haimin Hu tells us about his analysis on the algorithmic foundations of human-centered autonomy and his plans for future initiatives, and offers some recommendation for PhD college students trying to take the following step of their profession.

Might you give us an outline of the analysis you carried out throughout your PhD?

My PhD analysis, carried out below the supervision of Professor Jaime Fernández Fisac within the Princeton Protected Robotics Lab, focuses on the algorithmic foundations of human-centered autonomy. By integrating dynamic sport concept with machine studying and safety-critical management, my work goals to make sure autonomous programs, from self-driving automobiles to drones and quadrupedal robots, are performant, verifiable, and reliable when deployed in human-populated house. The core precept of my PhD analysis is to plan robots’ movement within the joint house of each bodily and knowledge states, actively guaranteeing security as they navigate unsure, altering environments and work together with people. Its key contribution is a unified algorithmic framework—backed by sport concept—that enables robots to securely work together with their human friends, adapt to human preferences and objectives, and even assist people refine their expertise. Particularly, my PhD work contributes to the next areas in human-centered autonomy and multi-agent programs:

• Reliable human–robotic interplay: Planning protected and environment friendly robotic trajectories by closing the computation loop between bodily human-robot interplay and runtime studying that reduces the robotic’s uncertainty in regards to the human.
• Verifiable neural security evaluation for advanced robotic programs: Studying sturdy neural controllers for robots with high-dimensional dynamics; guaranteeing their training-time convergence and deployment-time security.
• Scalable interactive planning below uncertainty: Synthesizing game-theoretic management insurance policies for advanced and unsure human–robotic programs at scale.

Was there a mission (or facet of your analysis) that was significantly attention-grabbing?

Security in human-robot interplay is very tough to outline, as a result of it hinges on an, I’d say, nearly unanswerable query: How protected is protected sufficient when people may behave in arbitrary methods? To provide a concrete instance: Is it enough if an autonomous car can keep away from hitting a fallen bike owner 99.9% of the time? What if this charge can solely be achieved by the car all the time stopping and ready for the human to maneuver out of the way in which?

I might argue that, for reliable deployment of robots in human-populated house, we have to complement customary statistical strategies with clear-cut sturdy security assurances below a vetted set of operation situations as nicely established as these of bridges, energy vegetation, and elevators. We’d like runtime studying to reduce the robotic’s efficiency loss brought on by safety-enforcing maneuvers; this requires algorithms that may cut back the robotic’s inherent uncertainty induced by its human friends, for instance, their intent (does a human driver need to merge, lower behind, or keep within the lane?) or response (if the robotic comes nearer, how will the human react?). We have to shut the loop between the robotic’s studying and decision-making in order that it could optimize effectivity by anticipating how its ongoing interplay with the human might have an effect on the evolving uncertainty, and in the end, its long-term efficiency.

What made you need to examine AI, and the realm of human-centered robotic programs specifically?

I’ve been fascinated by robotics and clever programs since childhood, once I’d spend total days watching sci-fi anime like Cellular Go well with Gundam, Neon Genesis Evangelion, or Future GPX Cyber Formulation. What captivated me wasn’t simply the futuristic expertise, however the imaginative and prescient of AI as a real companion—augmenting human talents moderately than changing them. Cyber Formulation specifically planted the thought of human-AI co-evolution in my thoughts: an AI co-pilot that not solely helps a human driver navigate high-speed, high-stakes environments, but in addition adapts to the motive force’s model over time, in the end making the human a greater racer and deepening mutual belief alongside the way in which. At this time, throughout my collaboration with Toyota Analysis Institute (TRI), I work on human-centered robotics programs that embody this precept: designing AI programs that collaborate with folks in dynamic, safety-critical settings by quickly aligning with human intent via multimodal inputs, from bodily help to visible cues and language suggestions, bringing to life the very concepts that when lived in my childhood creativeness.

You’ve landed a school place at Johns Hopkins College (JHU) – congratulations! Might you speak a bit in regards to the technique of job looking, and maybe share some recommendation and insights for PhD college students who could also be at the same stage of their profession?

The job search was positively intense but in addition deeply rewarding. My recommendation to PhD college students: begin pondering early in regards to the type of long-term affect you need to make, and act early in your utility bundle and job speak. Additionally, be sure to speak to folks, particularly your senior colleagues and friends on the job market. I personally benefited quite a bit from the next assets:

Do you might have an concept of the analysis initiatives you’ll be engaged on at JHU?

I want to assist create a future the place people can unquestionably embrace the presence of robots round them. In the direction of this imaginative and prescient, my lab at JHU will examine the next matters:

• Uncertainty-aware interactive movement planning: How can robots plan protected and environment friendly movement by accounting for his or her evolving uncertainty, in addition to their potential to cut back it via future interplay, sensing, communication, and studying?
• Human–AI co-evolution and co-adaptation: How can embodied AI programs study from human teammates whereas serving to them refine current expertise and purchase new ones in a protected, personalised method?
• Protected human-compatible autonomy: How can autonomous programs guarantee prescribed security whereas remaining aligned with human values and attuned to human cognitive limitations?
• Scalable and generalizable strategic decision-making: How can multi-robot programs make protected, coordinated selections in dynamic, human-populated environments?

How was the expertise attending the AAAI Doctoral Consortium?

I had the privilege of attending the 2025 AAAI Doctoral Consortium, and it was an extremely precious expertise. I’m particularly grateful to the organizers for curating such a considerate and supportive atmosphere for early-career researchers. The spotlight for me was the mentoring session with Dr Ming Yin (postdoc at Princeton, now school at Georgia Tech CSE), whose insights on navigating the unsure and aggressive job market had been each encouraging and eye-opening.

Might you inform us an attention-grabbing (non-AI associated) reality about you?

I’m keen about snowboarding. I realized to ski primarily by vision-based imitation studying from a chairlift, although I’m positively paying the worth now for poor generalization! At some point, I hope to construct an exoskeleton that teaches me to ski higher whereas protecting me protected on the double black diamonds.

About Haimin

Haimin Hu is an incoming Assistant Professor of Laptop Science at Johns Hopkins College, the place he’s additionally a member of the Information Science and AI Institute, the Institute for Assured Autonomy, and the Laboratory for Computational Sensing and Robotics. His analysis focuses on the algorithmic foundations of human-centered autonomy. He has acquired a number of awards and recognitions, together with a 2025 Robotics: Science and Programs Pioneer, a 2025 Cyber-Bodily Programs Rising Star, and a 2024 Human-Robotic Interplay Pioneer. Moreover, he has served as an Affiliate Editor for IEEE Robotics and Automation Letters since his fourth 12 months as a PhD pupil. He obtained a PhD in Electrical and Laptop Engineering from Princeton College in 2025, an MSE in Electrical Engineering from the College of Pennsylvania in 2020, and a BE in Digital and Info Engineering from ShanghaiTech College in 2018.