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Franck Djeumou

11 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 ↗
2026

Reference-Free, Long-Horizon Trajectory Optimization for Aggressive Autonomous Driving in Milliseconds

Prayag Sharma, Jon Goh, Franck Djeumou
ICRA 2026poster

Autonomous vehicles must generate long-horizon and dynamically feasible trajectories in real time—even when operating at the limits of vehicle handling—to ensure safe operation in adverse conditions. However, existing work rarely quantifies the computational demands of generating such trajectories w…

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

Neural Stochastic Differential Equations for Uncertainty-Aware Offline RL

Cevahir Koprulu, Franck Djeumou, ufuk topcu
ICLR 2025poster

Offline model-based reinforcement learning (RL) offers a principled approach to using a learned dynamics model as a simulator to optimize a control policy. Despite the near-optimal performance of existing approaches on benchmarks with high-quality datasets, most struggle on datasets with low state-…

Cited by 0Source ↗PDF ↗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 ↗
2023

Autonomous Drifting with 3 Minutes of Data via Learned Tire Models

Franck Djeumou, Jonathan Y.M. Goh, Ufuk Topcu, Avinash Balachandran
ICRA 2023poster

Near the limits of adhesion, the forces generated by a tire are nonlinear and intricately coupled. Efficient and accurate modelling in this region could improve safety, especially in emergency situations where high forces are required. To this end, we propose a novel family of tire force models base…

Cited by 25Source ↗Scholar ↗
2023

How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations

Franck Djeumou, Cyrus Neary, ufuk topcu
CoRL 2023oral

We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs)---SDEs whose drift and diffusion terms are both parametrized by neural networks. We construct the drift term to leverage a priori physics knowledge as inductive bias, and w…

Cited by 13Source ↗Scholar ↗
2022

Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs

Franck Djeumou, Cyrus Neary, Eric Goubault, Sylvie Putot, Ufuk Topcu
IJCAI 2022poster

Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data. However, every forward evaluation of a NODE requires numerical integration of…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2020

Probabilistic Swarm Guidance Subject to Graph Temporal Logic Specifications

Franck Djeumou, Zhe Xu, Ufuk Topcu
RSS 2020poster

As the number of agents comprising a swarm increases, individual-agent-based control techniques for collective task completion become computationally intractable. We study a setting in which the agents move along the nodes of a graph, and the high-level task specifications for the swarm are expresse…

Cited by 24Source ↗PDF ↗code ↗Scholar ↗