Franck Djeumou | DeepAI

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∙ 06/10/2023

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

We present a framework and algorithms to learn controlled dynamics model...

0 Franck Djeumou, et al. ∙

∙ 06/10/2023

Autonomous Drifting with 3 Minutes of Data via Learned Tire Models

Near the limits of adhesion, the forces generated by a tire are nonlinea...

0 Franck Djeumou, et al. ∙

∙ 01/09/2023

Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control

Data-driven control algorithms use observations of system dynamics to co...

0 Adam J. Thorpe, et al. ∙

∙ 12/30/2022

Task-Guided IRL in POMDPs that Scales

In inverse reinforcement learning (IRL), a learning agent infers a rewar...

0 Franck Djeumou, et al. ∙

∙ 01/14/2022

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

Neural ordinary differential equations (NODEs) – parametrizations of dif...

0 Franck Djeumou, et al. ∙

∙ 09/14/2021

Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling

Effective inclusion of physics-based knowledge into deep neural network ...

0 Franck Djeumou, et al. ∙

∙ 06/29/2021

Probabilistic Control of Heterogeneous Swarms Subject to Graph Temporal Logic Specifications: A Decentralized and Scalable Approach

We develop a probabilistic control algorithm, , for swarms of agents wit...

0 Franck Djeumou, et al. ∙

∙ 06/19/2021

Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information

We develop a learning-based control algorithm for unknown dynamical syst...

0 Franck Djeumou, et al. ∙

∙ 05/28/2021

Task-Guided Inverse Reinforcement Learning Under Partial Information

We study the problem of inverse reinforcement learning (IRL), where the ...

0 Franck Djeumou, et al. ∙

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