Publications

Synthesis and Verification of Robust-Adaptive Safe Controllers
Simin Liu*, Kai S. Yun*, John M. Dolan, Changliu Liu
European Control Conference 2024 | arxiv

We investigate controller synthesis for dynamical systems with uncertain parameters. We designed an optimization algorithm for generating robust-adaptive safe controllers that can guarantee safety in the presence of uncertainties, without being overly conservative. Our controller performs 55% better compared to popular robust controllers.

Learning Model Predictive Control with Error Dynamics Regression for Autonomous Racing
Haoru Xue, Edward L. Zhu, John M. Dolan, Francesco Borrelli
ICRA 2024 | arxiv | website | code | social media | talk

Use MPC + robot learning to explore optimal policy and dynamics model online safely in Sim2Real. Learn the handling limit of extreme driving like a professional race car driver. Deployed on a full-size race car!

Safe Deep Policy Adaptation
Wenli Xiao*, Tairan He*, John M. Dolan, Guanya Shi
ICRA 2024 | arxiv | website | code | social media | talk

This paper jointly tackles policy adaptation and safe reinforcement learning with safety guarantees. Comprehensive experiments on (1) classic control problems (Inverted Pendulum), (2) simulation benchmarks (Safety Gym), and (3) a real-world agile robotics platform (RC Car) demonstrate great superiority of SafeDPA in both safety and task performance, over state-of-the-art baselines.

Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine
Dvij Kalaria, Qin Lin, John M. Dolan
ICRA 2024 | arxiv | website | code | social media

A semi-learning based approach that learns a residual dynamics model that can quickly adapt to changes in environment for autonomous racing like tire degradation, weather change etc.

WROOM: An Autonomous Driving Approach for Off-Road Navigation
Dvij Kalaria, Shreya Sharma, Sarthak Bhagat, Haoru Xue, John M. Dolan
ICRA 2024 Workshop | arxiv | website | code

WROOM brings a gym environment for training off-road driving RL policy. We use PPO + CBF to train an end-to-end agent to safely navigate in the real world.

Safe Control Under Input Limits with Neural Control Barrier Functions
Simin Liu, Changliu Liu, John M. Dolan
CoRL 2023 | arxiv | code | slides

We created a scalable dynamics adaptation technique using adversarial training of neural CBFs. Essentially, our method trades the theoretical guarantees of safety for scalability and strong empirical guarantees (>99% safe). Currently, this class of methods has been shown to scale to >20D. This includes complex systems, like balancing quadrotors and many-linked manipulators.

Towards Safety Assured End-to-End Vision-Based Control for Autonomous Racing
Dvij Kalaria, Qin Lin, John M. Dolan
IFAC Congress 2023 | arxiv | code | social media

A model-based CBF safety wrapper on model-free automated imitation learning makes it very sample efficient.