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
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.
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.
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.
Skatebots: Development Log
A running technical log of xLab and DEEP Robotics' legged-robot work: mapping, multi-floor navigation, automatic gait switching, our own continuous route follower and language-guided navigation.
Latest News
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