SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
This letter introduces a safe information-theoretic learning model predictive control (SIT-LMPC) algorithm for iterative tasks.
About Us
We are xLAB, the Safe Autonomous Systems Lab at the University of Pennsylvania. We combine machine learning, control theory, and formal methods to develop safe, high-performance autonomous systems, with applications in autonomous vehicles, medical devices, and human–AI collaboration. Our research connects theory with real-world systems. We are part of Penn’s PRECISE Center and GRASP Laboratory.
Highlights
Patient-Specific Heart Modeling for Arrhythmia Ablation
Process of patient-specific heart modeling is: extract patient data to tune heart model parameters, next, initiate arrhythmia (the heart's initial state is also extracted from patient data), then solve the heart model equations to simulate patient-specific arrhythmias.
AI Coaching for Accelerating Human Skill Development with Reinforcement Learning
A game-theoretic framework for AI-assisted skill acquisition in high-speed drone racing and other cyber-physical tasks.
RetroLifts: Low-Cost Forklift Retrofits for Scalable Autonomous Brownfield Deployments
A low-cost hardware and software retrofit solution enabling scalable autonomous forklift fleets in brownfield warehouse environments.
MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts
Learning autonomous racing policies from complementary imperfect experts, with F1TENTH benchmarks for imitation learning and reinforcement learning.
Fly-by-Logic: Safe Planning and Collision Avoidance for Drone Fleets
My team has designed safe and efficient Learning-based decentralized collision avoidance for scalable urban air mobility.
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