6 papers
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…
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…
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 the capabilities of robot algorithms. While aerial robotics simulators have evolved…
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…
scipy.spatial.transform: Differentiable Framework-Agnostic 3D Transformations in Python
Martin Schuck, Alexander von Rohr, Angela P. Schoellig
Three-dimensional rigid-body transforms, i.e. rotations and translations, are central to modern differentiable machine learning pipelines in robotics, vision, and simulation. Howev…
SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography
Martin Schuck, Dinushka Orrin Dahanaggamaarachchi, Ben Sprenger +3
Drone swarm performances -- synchronized, expressive aerial displays set to music -- have emerged as a captivating application of modern robotics. Yet designing smooth, safe choreo…