@inproceedings{zhang2024uduc,

title = {UDUC: An Uncertainty-driven Approach for Learning-based Robust Control},

author = {Yuan Zhang and Jasper Hoffman and Joschka Boedecker},

url = {https://arxiv.org/abs/2405.02598},

doi = {10.3233/FAIA241018},

year  = {2024},

date = {2024-10-24},

urldate = {2024-10-24},

booktitle = {ECAI 2024 - 27th European Conference on Artificial Intelligence - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024)},

volume = {392},

pages = {4402-4409},

publisher = {IOS Press},

address = {Santiago de Compostela, Spain},

series = {Frontiers in Artificial Intelligence and Applications},

abstract = {Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE) models offer a promising approach for modelling system dynamics, showcasing the ability to capture uncertainty and scalability in high-dimensional control scenarios. However, PE models are susceptible to mode collapse, resulting in non-robust control when faced with environments slightly different from the training set. In this paper, we introduce the uncertainty-driven robust control (UDUC) loss as an alternative objective for training PE models, drawing inspiration from contrastive learning. We analyze the robustness of the UDUC loss through the lens of robust optimization and evaluate its performance on the challenging real-world reinforcement learning (RWRL) benchmark, which involves significant environmental mismatches between the training and testing environments.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hoffmann_plannetx_2024,

title = {PlanNetX: Learning an efficient neural network planner from MPC for longitudinal control},

author = {Jasper Hoffman and Diego Fernandez Clausen and Julien Brosseit and Julian Bernhard and Klemens Esterle and Moritz Werling and Michael Karg and Joschka Boedecker},

url = {https://proceedings.mlr.press/v242/hoffmann24a.html

https://proceedings.mlr.press/v242/hoffmann24a/hoffmann24a.pdf},

year  = {2024},

date = {2024-07-15},

urldate = {2024-07-15},

booktitle = {Proceedings of the 6th Annual Learning for Dynamics & Control Conference},

pages = {1214-1227},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

abstract = {Model predictive control (MPC) is a powerful, optimization-based approach for controlling dynamical systems. However, the computational complexity of online optimization can be problematic on embedded devices. Especially, when we need to guarantee fixed control frequencies. Thus, previous work proposed to reduce the computational burden using imitation learning (IL) approximating the MPC policy by a neural network. In this work, we instead learn the whole planned trajectory of the MPC. We introduce a combination of a novel neural network architecture PlanNetX and a simple loss function based on the state trajectory that leverages the parameterized optimal control structure of the MPC. We validate our approach in the context of autonomous driving by learning a longitudinal planner and benchmarking it extensively in the CommonRoad simulator using synthetic scenarios and scenarios derived from real data. Our experimental results show that we can learn the open-loop MPC trajectory with high accuracy while improving the closed-loop performance of the learned control policy over other baselines like behavior cloning.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Reiter2023Hierarchical,

title = {A Hierarchical Approach for Strategic Motion Planning in Autonomous Racing},

author = {Rudolf Reiter and Jasper Hoffman and Joschka Boedecker and Moritz Diehl},

doi = {10.23919/ECC57647.2023.10178143},

isbn = {978-3-907144-08-4},

year  = {2023},

date = {2023-07-17},

urldate = {2023-07-17},

booktitle = {2023 European Control Conference (ECC)},

pages = {1-8},

publisher = {IEEE},

address = {Bucharest, Romania},

abstract = {We present an approach for safe trajectory planning, where a strategic task related to autonomous racing is learned sample efficiently within a simulation environment. A high-level policy, represented as a neural network, outputs a reward specification that is used within the function of a parametric nonlinear model predictive controller. By including constraints and vehicle kinematics in the nonlinear program, we can guarantee safe and feasible trajectories related to the used model. Compared to classical reinforcement learning, our approach restricts the exploration to safe trajectories, starts with an excellent prior performance and yields complete trajectories that can be passed to a tracking lowest-level controller. We do not address the lowest-level controller in this work and assume perfect tracking of feasible trajectories. We show the superior performance of our algorithm on simulated racing tasks that include high-level decision-making. The vehicle learns to efficiently overtake slower vehicles and avoids getting overtaken by blocking faster ones.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ghezzi2023b,

title = {Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation},

author = {Andrea Ghezzi and Jasper Hoffman and Jonathan Frey and Joschka Boedecker and Moritz Diehl},

url = {https://doi.org/10.48550/arXiv.2304.01782},

doi = {10.1109/CDC49753.2023.10383323},

isbn = {979-8-3503-0124-3},

year  = {2023},

date = {2023-01-19},

urldate = {2023-01-19},

booktitle = {2023 Conference on Decision and Control (CDC)},

pages = {4766-4771},

publisher = {IEEE},

address = {Singapore, Singapore},

abstract = {This work presents a novel loss function for learning nonlinear Model Predictive Control policies via Imitation Learning. Standard approaches to Imitation Learning neglect information about the expert and generally adopt a loss function based on the distance between expert and learned controls. In this work, we present a loss based on the Q-function directly embedding the performance objectives and constraint satisfaction of the associated Optimal Control Problem (OCP). However, training a Neural Network with the Q-loss requires solving the associated OCP for each new sample. To alleviate the computational burden, we derive a second Q-loss based on the Gauss-Newton approximation of the OCP resulting in a faster training time. We validate our losses against Behavioral Cloning, the standard approach to Imitation Learning, on the control of a nonlinear system with constraints. The final results show that the Q-function-based losses significantly reduce the amount of constraint violations while achieving comparable or better closed-loop costs.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}