382 citations · 1.2k across the 106 of their papers we have counts for
6 papers · 1 filter
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles
Johanna Bethge, Maik Pfefferkorn, Alexander Rose +2
We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs…
Model-Based Reinforcement Learning via Stochastic Hybrid Models
Hany Abdulsamad, Jan Peters
Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently succe…
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk
Joe Watson, Jan Peters
Discrete-time stochastic optimal control remains a challenging problem for general, nonlinear systems under significant uncertainty, with practical solvers typically relying on the…
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions
Hany Abdulsamad, Tim Dorau, Boris Belousov +2
Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications, ranging from autonomous driving to full-body humanoid cont…
Chance-Constrained Trajectory Optimization for Non-linear Systems with Unknown Stochastic Dynamics
Onur Celik, Hany Abdulsamad, Jan Peters
Iterative trajectory optimization techniques for non-linear dynamical systems are among the most powerful and sample-efficient methods of model-based reinforcement learning and app…
Multi-modal filtering for non-linear estimation
Sanket Kamthe, Jan Peters, Marc P Deisenroth
Multi-modal densities appear frequently in time series and practical applications. However, they cannot be represented by common state estimators, such as the Extended Kalman Filte…