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

On this interview sequence, we’re assembly a number of the AAAI/SIGAI Doctoral Consortium individuals to search 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 tasks, and provides some recommendation for PhD college students trying to take the following step of their profession.

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

My PhD analysis, performed beneath 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 recreation principle with machine studying and safety-critical management, my work goals to make sure autonomous techniques, from self-driving autos to drones and quadrupedal robots, are performant, verifiable, and reliable when deployed in human-populated area. The core precept of my PhD analysis is to plan robots’ movement within the joint area of each bodily and data 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 recreation principle—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 techniques:

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

Was there a challenge (or facet of your analysis) that was notably fascinating?

Security in human-robot interplay is particularly troublesome to outline, as a result of it hinges on an, I’d say, virtually unanswerable query: How secure is secure sufficient when people may behave in arbitrary methods? To offer a concrete instance: Is it adequate if an autonomous automobile can keep away from hitting a fallen bicycle owner 99.9% of the time? What if this fee can solely be achieved by the automobile 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 area, we have to complement customary statistical strategies with clear-cut strong security assurances beneath a vetted set of operation circumstances as properly established as these of bridges, energy crops, and elevators. We want runtime studying to reduce the robotic’s efficiency loss attributable to 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, minimize 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 possibly optimize effectivity by anticipating how its ongoing interplay with the human might have an effect on the evolving uncertainty, and finally, its long-term efficiency.

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

I’ve been fascinated by robotics and clever techniques since childhood, after I’d spend whole days watching sci-fi anime like Cell Swimsuit Gundam, Neon Genesis Evangelion, or Future GPX Cyber Formulation. What captivated me wasn’t simply the futuristic know-how, however the imaginative and prescient of AI as a real accomplice—augmenting human talents fairly 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 additionally adapts to the driving force’s fashion over time, finally making the human a greater racer and deepening mutual belief alongside the way in which. At present, throughout my collaboration with Toyota Analysis Institute (TRI), I work on human-centered robotics techniques that embody this precept: designing AI techniques that collaborate with folks in dynamic, safety-critical settings by quickly aligning with human intent by way of 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 college place at Johns Hopkins College (JHU) – congratulations! Might you speak a bit concerning 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 additionally deeply rewarding. My recommendation to PhD college students: begin considering early concerning the sort of long-term influence you need to make, and act early in your software 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 sources:

Do you might have an concept of the analysis tasks 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 secure and environment friendly movement by accounting for his or her evolving uncertainty, in addition to their capability to cut back it by way of future interplay, sensing, communication, and studying?
• Human–AI co-evolution and co-adaptation: How can embodied AI techniques study from human teammates whereas serving to them refine present expertise and purchase new ones in a secure, customized method?
• Protected human-compatible autonomy: How can autonomous techniques 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 techniques make secure, 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 invaluable expertise. I’m particularly grateful to the organizers for curating such a considerate and supportive setting 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 have been each encouraging and eye-opening.

Might you inform us an fascinating (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 preserving me secure on the double black diamonds.

About Haimin

Haimin Hu is an incoming Assistant Professor of Pc 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 Methods Pioneer, a 2025 Cyber-Bodily Methods 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 scholar. He obtained a PhD in Electrical and Pc 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.