7 citations · 10 across the 10 of their papers we have counts for
10 papers
WarpMPC: Large-Batch MPC on GPU via ADMM with Unrolled Factorization
Henrik Hose, Se Hwan Jeon, Charles Khazoom +2
This paper introduces numerical optimizations for maximizing throughput on GPU when solving large batches (10,000 to over 100,000) of sequential quadratic programming (SQP) iterati…
Learning to Race in Minutes: Infoprop Dyna on the Mini Wheelbot
Devdutt Subhasish, Henrik Hose, Sebastian Trimpe
Reinforcement Learning (RL) has the potential to enable robots with fast, nonlinear, and unstable dynamics to reach the limits of their performance. However, most recent advances r…
Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank +2
Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on fir…
The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning
Henrik Hose, Paul Brunzema, Devdutt Subhasish +1
The development of robust learning-based control algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a signif…
Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
Henrik Hose, Paul Brunzema, Alexander von Rohr +3
Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network, removing the need to solve an expensive optimization problem at runtime. Howeve…
Diffusion-Based Approximate MPC: Fast and Consistent Imitation of Multi-Modal Action Distributions
Pau Marquez Julbe, Julian Nubert, Henrik Hose +2
Approximating model predictive control (MPC) using imitation learning (IL) allows for fast control without solving expensive optimization problems online. However, methods that use…
The Mini Wheelbot: A Testbed for Learning-based Balancing, Flips, and Articulated Driving
Henrik Hose, Jan Weisgerber, Sebastian Trimpe
The Mini Wheelbot is a balancing, reaction wheel unicycle robot designed as a testbed for learning-based control. It is an unstable system with highly nonlinear yaw dynamics, non-h…
Feedforward Controllers from Learned Dynamic Local Model Networks with Application to Excavator Assistance Functions
Leon Greiser, Ozan Demir, Benjamin Hartmann +2
Complicated first principles modelling and controller synthesis can be prohibitively slow and expensive for high-mix, low-volume products such as hydraulic excavators. Instead, in…
Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining
Henrik Hose, Alexander Gräfe, Sebastian Trimpe
Model Predictive Control (MPC) is a method to control nonlinear systems with guaranteed stability and constraint satisfaction but suffers from high computation times. Approximate M…
Approximate non-linear model predictive control with safety-augmented neural networks
Henrik Hose, Johannes Köhler, Melanie N. Zeilinger +1
Model predictive control (MPC) achieves stability and constraint satisfaction for general nonlinear systems, but requires computationally expensive online optimization. This paper…