6 citations · 6 across the 8 of their papers we have counts for
5 papers · 1 filter
Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics
Aleš Kučera, Karel Zimmermann
Three properties determine whether a differentiable simulator can drive gradient-based optimization through contact: simulation accuracy, gradient reliability, and per-iteration co…
ProTerrain: Probabilistic Physics-Informed Rough Terrain World Modeling
Golnaz Raja, Ruslan Agishev, Miloš Prágr +4
Uncertainty-aware robot motion prediction is crucial for downstream traversability estimation and safe autonomous navigation in unstructured, off-road environments, where terrain i…
Manual, Semi or Fully Autonomous Flipper Control? A Framework for Fair Comparison
Valentýn Číhala, Martin Pecka, Tomáš Svoboda +1
We investigated the performance of existing semi- and fully autonomous methods for controlling flipper-based skid-steer robots. Our study involves reimplementation of these methods…
FusionForce: End-to-end Differentiable Neural-Symbolic Layer for Trajectory Prediction
Ruslan Agishev, Karel Zimmermann
We propose end-to-end differentiable model that predicts robot trajectories on rough offroad terrain from camera images and/or lidar point clouds. The model integrates a learnable…
MonoForce: Self-supervised Learning of Physics-informed Model for Predicting Robot-terrain Interaction
Ruslan Agishev, Karel Zimmermann, Vladimír Kubelka +2
While autonomous navigation of mobile robots on rigid terrain is a well-explored problem, navigating on deformable terrain such as tall grass or bushes remains a challenge. To addr…