John K. Subosits — accepted AI-conference papers

2025

First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling

Alexander Davydov, Franck Djeumou, Marcus Greiff, Makoto Suminaka, Michael Thompson, John K. Subosits +1

RA-L 2025

Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. However, when deployed on unstable systems, online adaptation may not be fast enough to ensure reliable simultaneous learning and control. For example, a controller o

Cited by 8 Source ↗Scholar ↗

2025

Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies

Franck Djeumou, Michael Thompson, Makoto Suminaka, John K. Subosits

ICRA 2025

The skill to drift a car-i.e., operate in a state of controlled oversteer like professional drivers-could give future autonomous cars maximum flexibility when they need to retain control in adverse conditions or avoid collisions. We investigate real-time drifting strategies that put the car where ne

Cited by 4 Source ↗Scholar ↗

2025

Risk-Averse Model Predictive Control for Racing in Adverse Conditions

Thomas Lew, Marcus Greiff, Franck Djeumou, Makoto Suminaka, Michael Thompson, John K. Subosits

ICRA 2025

Model predictive control (MPC) algorithms can be sensitive to model mismatch when used in challenging nonlinear control tasks. In particular, the performance of MPC for vehicle control at the limits of handling suffers when the underlying model overestimates the vehicle's performance capabilities. I

Cited by 7 Source ↗Scholar ↗