1 citations · 3 across the 22 of their papers we have counts for
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DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning in Robot Manipulation
Nisarga Nilavadi, Ralf Römer, Moritz Reuss +5
Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-gr…
A Decade of Bayesian Optimization for Controller Tuning and Robot Learning: Tutorial, Review, and Future Prospects
David Stenger, Paul Brunzema, Johanna Menn +3
In the past decade, Bayesian optimization (BO) has emerged as a powerful and adaptable framework for automatic controller tuning and robot learning. This article offers a comprehen…
Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL
Martin Schuck, Maks Sorokin, Simone Manni +5
Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, man…
Learning Where it Matters: Autonomous Data Collection and Offline Reinforcement Learning for Local Contact-Rich Manipulation
Oliver Hausdörfer, Linus Schwarz, Gabor Marko +7
Learned policies trained end-to-end on large datasets often remain brittle in high-precision contact-rich tasks. Further, they typically require large amounts of manually collected…
EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal
Alex Brandes, Haig Conti Georges Sajelian, Manthan Patel +11
Deploying humanoid robots in unstructured terrain remains an open problem. While classic reinforcement learning struggles with the sheer complexity of real-world interactions, more…
Uncertainty Quantification for Flow-Based Generalist Robot Policies
Ralf Römer, Maximilian Seeliger, Saida Liu +5
Generalist robot policies, such as vision-language-action models (VLAs) and world-action models (WAMs), combine powerful pretrained backbones with expressive generative action head…
LoComposition: Terrain-Adaptive Energy-Efficient Quadruped Locomotion without Gait Priors
Loukas Kordos, Leonard T. Franz, Simon Rappenecker +4
Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation…
Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX
Martin Schuck, Marcel P. Rath, Yufei Hua +3
High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing robots' capabilities. While aerial robotics simulators have evolved to support speci…
Learning from Demonstrations over Riemannian Manifolds using Neural ODEs: An Extended Abstract
Diana Cuervo Espinosa, Mahathi Anand, Angela P. Schoellig
Learning from demonstratins (LfD) is usually performed over Euclidean spaces, while the robot state, e.g. orientation, naturally evolves over curved spaces. Therefore, to ensure na…
LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting
Hannah Schieber, Dominik Frischmann, Victor Schaack +2
Autonomous robots in unknown indoor environments require both reliable collision avoidance and object-level understanding. Classical representations such as TSDF support safe plann…
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…
CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion
Ralf Römer, Yi Zhang, Yuming Li +1
To teach robots complex manipulation tasks, a common approach is to fine-tune a pre-trained vision-language-action model (VLA) on task-specific data. However, since this recipe upd…
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…
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…
A Primer on SO(3) Action Representations in Deep Reinforcement Learning
Martin Schuck, Sherif Samy, Angela P. Schoellig
Many robotic control tasks require policies to act on orientations, yet the geometry of SO(3) makes this nontrivial. Because SO(3) admits no global, smooth, minimal parameterizatio…
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…
Synergistic Action and Style Priors for Learning Legged Locomotion
Oliver Hausdörfer, Alexander von Rohr, Éric Lefort +1
Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algori…
Reinforcement Learning with Lie Group Orientations for Robotics
Martin Schuck, Jan Brüdigam, Sandra Hirche +1
Handling orientations of robots and objects is a crucial aspect of many applications. Yet, ever so often, there is a lack of mathematical correctness when dealing with orientations…