1 citations · 1 across the 5 of their papers we have counts for
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From Demonstrations to Safe Deployment: Path-Consistent Safety Filtering for Diffusion Policies
Ralf Römer, Julian Balletshofer, Jakob Thumm +3
Diffusion policies (DPs) achieve state-of-the-art performance on complex manipulation tasks by learning from large-scale demonstration datasets, often spanning multiple embodiments…
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…
Flying through Moving Gates without Full State Estimation
Ralf Römer, Tim Emmert, Angela P. Schoellig
Autonomous drone racing requires powerful perception, planning, and control and has become a benchmark and test field for autonomous, agile flight. Existing work usually assumes st…