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.
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
AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shift
An exploration of conformal prediction techniques applied to robotics for improved decision-making and uncertainty quantification.
STL-SVPIO: Signal Temporal Logic Guided Stein Variational Path Integral Optimization
Signal Temporal Logic (STL) enables formal specification of complex spatiotemporal constraints for robotic task planning.
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.
Failure-Aware Iterative Learning of State-Control Invariant Sets
A learning-from-failure framework for learning the maximal control invariant set and its invariance-preserving inputs.
Nonplanar Model Predictive Control for Autonomous Vehicles with Recursive Sparse Gaussian Process Dynamics
A geometry-aware MPC framework for autonomous vehicles on nonplanar terrain, using recursive sparse Gaussian Processes for real-time adaptation and improved tracking.
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