5 papers
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…
Failure Prediction at Runtime for Generative Robot Policies
Ralf Römer, Adrian Kobras, Luca Worbis +1
Imitation learning (IL) with generative models, such as diffusion and flow matching, has enabled robots to perform complex, long-horizon tasks. However, distribution shifts from un…
ARMimic: Learning Robotic Manipulation from Passive Human Demonstrations in Augmented Reality
Rohan Walia, Yusheng Wang, Ralf Römer +3
Imitation learning is a powerful paradigm for robot skill acquisition, yet conventional demonstration methods--such as kinesthetic teaching and teleoperation--are cumbersome, hardw…
CRISP -- Compliant ROS2 Controllers for Learning-Based Manipulation Policies and Teleoperation
Daniel San José Pro, Oliver Hausdörfer, Ralf Römer +3
Learning-based controllers, such as diffusion policies and vision-language action models, often generate low-frequency or discontinuous robot state changes. Achieving smooth refere…
Diffusion Predictive Control with Constraints
Ralf Römer, Alexander von Rohr, Angela P. Schoellig
Diffusion models have become popular for policy learning in robotics due to their ability to capture high-dimensional and multimodal distributions. However, diffusion policies are…