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Makoto Suminaka

5 accepted papers

2026

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 Subosits +1
ICRA 2026poster

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 0Source ↗PDF ↗Scholar ↗
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 8Source ↗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 4Source ↗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 7Source ↗Scholar ↗
2024

One Model to Drift Them All: Physics-Informed Conditional Diffusion Model for Driving at the Limits

Franck Djeumou, Thomas Jonathan Lew, NAN DING, Michael Thompson, Makoto Suminaka, Marcus Greiff +1
CoRL 2024poster

Enabling autonomous vehicles to reliably operate at the limits of handling— where tire forces are saturated — would improve their safety, particularly in scenarios like emergency obstacle avoidance or adverse weather conditions. However, unlocking this capability is challenging due to the task's dyn…

Cited by 8Source ↗Scholar ↗