From the 1 of 19 linked papers with an AI index.
19 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…
Data and Learning Where it Matters for 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 tasks and struggle with generalization. We find that these limitations largely stem fro…
EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal
Alex Brandes, Haig Conti Georges Sajelian, Manthan Patel +11
The paper introduces EgoHTR, a dataset of egocentric 4D human motion captured in complex, unstructured terrain using wearable sensors and a portable 3D scanner, and demonstrates it…
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…
Uncertainty Quantification for Flow-Based Vision-Language-Action Models
Ralf Römer, Maximilian Seeliger, Saida Liu +5
Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite th…
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…