Latest

• Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies
• First, Learn What You Don’t Know: Active Information Gathering for Driving at the Limits of Handling
• Risk-Averse Model Predictive Control for Racing in Adverse Conditions
• One Model to Drift Them All: Physics-Informed Conditional Diffusion Model for Driving at the Limits
• How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations
• Autonomous Drifting with 3 Minutes of Data via Learned Tire Models
• Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs
• Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
• Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information
• Task-Guided Inverse Reinforcement Learning Under Partial Information
• Probabilistic control of heterogeneous swarms subject to graph temporal logic specifications: A decentralized and scalable approach
• On-the-fly, data-driven reachability analysis and control of unknown systems: an F-16 aircraft case study
• Learning-Based, Safety-Constrained Control from Scarce Data via Reciprocal Barriers
• Online synthesis for runtime enforcement of safety in multi-agent systems
• Safety-Constrained Learning and Control using Scarce Data and Reciprocal Barriers
• On-The-Fly Control of Unknown Systems: From Side Information to Performance Guarantees through Reachability
• On-The-Fly Control of Unknown Smooth Systems from Limited Data
• Blending Controllers via Multi-Objective Bandits
• Probabilistic Swarm Guidance Subject to Graph Temporal Logic Specifications
• Policy synthesis for switched linear systems with Markov decision process switching
• Safety Guarantees for Drones through Set-Based Formal Verification Methods
• Human-Embedded Autonomous Flight Under Formal Task Specifications