12 papers
Solving Markov Decision Processes with Future Information via MPC
Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt +1
Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based p…
Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study
Guanru Pan, Dirk Reinhardt, Sebastien Gros +1
This paper presents a data-driven framework for uncertainty propagation under unmeasured or statistically unmodeled (unstructured) disturbances. We consider residual disturbances,…
Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion
Wenqi Cai, Kyriakos G. Vamvoudakis, Sébastien Gros +1
In this paper, we propose a cost-matching approach for optimal humanoid locomotion within a Model Predictive Control (MPC)-based Reinforcement Learning (RL) framework. A parameteri…
Differentiable Nonlinear Model Predictive Control
Jonathan Frey, Katrin Baumgärtner, Gianluca Frison +5
The efficient computation of parametric solution sensitivities is a key challenge in the integration of learning-enhanced methods with nonlinear model predictive control (MPC), as…
CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
Akhil S Anand, Elias Aarekol, Martin Mziray Dalseg +2
Combinatorial sequential decision making problems are typically modeled as mixed integer linear programs (MILPs) and solved via branch and bound (B&B) algorithms. The inherent diff…
Direct transfer of optimized controllers to similar systems using dimensionless MPC
Josip Kir Hromatko, Shambhuraj Sawant, Šandor Ileš +1
Scaled model experiments are commonly used in various engineering fields to reduce experimentation costs and overcome constraints associated with full-scale systems. The relevance…