3 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…