Projects
National Science Foundation, Award #2533851, $440,000, Aug. 2026 – Jul. 2029. PI (co-PI: Dr. Brendon C Allen). We collaborate with Drs. Warren E Dixon and Wenqian Xue at University of Florida.
This NSF project aims to create a new mathematical and learning framework for interpreting and predicting how autonomous agents (people, animals, or machines) behave, from what they do, not how they function internally. It is built on the intentional stance, an idea rooted in philosophy-of-mind that explains behavior by attributing goals and beliefs to an agent, even as an imperfect or sub-optimal one. The project will bring transformative changes to how engineers model complex, adaptive, and imperfect decision-makers, moving beyond methods that assume that agents act optimally or that their inner workings are known. This will be achieved by combining machine learning, control theory, and neuroscience to jointly infer an agent’s evolving beliefs, goals, and strategies, while guaranteeing the models stay stable and reliable. The intellectual merit of the project includes a mathematical and algorithmic formulation of the intentional stance that learns time-varying beliefs, objectives, and control strategies with formal stability guarantees. The broader impacts of the project include next-generation assistive devices, such as exoskeletons that adapt to a user’s intent, which will improve quality of life for millions affected by walking impairments. Broader impacts also include new courses, undergraduate research training, open datasets, and outreach that help attract middle- and high-school students to STEM.
Existing methods for interpreting behavior have limitations. Inverse Reinforcement Learning, which infers an agent’s hidden objectives, usually casts decisions as Markov Decision Processes suited to high-level planning but not the continuous, moment-to-moment control that real autonomous systems require. Overcoming this limitation demands complex nested computation that adds delays and errors. Inverse Optimal Control handles continuous control while recovering the cost a controller minimizes but requires known dynamics and agent optimality. Yet real agents routinely violate these assumptions. This project develops Dynamic Deep Predictive-Intentional Stance Learning (DDP-ISL), which introduces time-varying belief dynamics as proxies to explain observed behaviors as if they were optimal, and to jointly infer the agent’s dynamic objective and control policy, rather than requiring optimality or known dynamics. These models will be learned using Lyapunov-based deep neural networks with real-time training algorithms that guarantee training stability. Inspired by the neuroscience free-energy principle, they will use their predictions to continually self-refine, improving adaptability and robustness. Two settings are addressed: one with observed states and control inputs observed, while the other one with only observed states. DDP-ISL, in both settings, will be applied to exoskeletons for individuals with gait asymmetries to drive the impaired leg to track the healthier leg’s behavior and learn its intent.
National Science Foundation, Award #2601585, $600,000, Oct. 2026 – Sep. 2029. Co-PI (PI: Dr. Xiaowen Gong).
Abstract: This award establishes a renewed Research Experience for Teachers (RET) Site at Auburn University. The site will provide unique and holistic research experiences for 24 middle school math and science teachers in the 7th-8th grades from rural areas of Alabama. The research focus is on smart humanoid and mobile robots enabled by cutting-edge technologies of artificial intelligence (AI) and machine learning (ML). The goals of the site are to equip teachers with knowledge and skills in AI and ML and robotics and promote their interests in these areas and facilitate teachers’ development and implementation of engaging project-based curricular modules for their classrooms.
The site will provide research experiences to eight (8) middle school math and science teachers in the 7th-8th grades each year via a six-week summer program and nine-month academic year follow-up, with the research focused on smart mobile robots based on AI and ML. The site has five primary objectives to reach its goals of providing a rigorous and engaging RET experience: 1) provide education and training activities on the fundamentals of AI/ML and robotics, and novel platforms of ML-based smart humanoid and mobile robots for research and education; 2) engage teachers in hands-on research projects on ML-based smart robots that match well with faculty mentors’ research projects; 3) allow teachers to collaborate with engineering and STEM education faculty to develop the project-based curricular modules; 4) foster teachers’ leadership and pedagogical skills via teacher leader mentoring and practice of teaching the RET curricular modules; 5) assist teachers to implement the RET curricular modules via academic year follow-up.