6 citations · 6 across the 8 of their papers we have counts for
10 papers
Propagating Elevation-Map Uncertainty Through the Contact Maximum in Closed Form
Aleš Kučera, Karel Zimmermann
Risk-aware planners score paths on uncertain elevation maps using the path cost's mean and standard deviation. Modeling rigid contact, however, requires computing a maximum over se…
Occlusion-Aware, Quasi-Static, Stability-Oriented Trajectory Planning on Uneven Terrain
Amith Manoharan, Chinmay Mundane, Aayush Bahukhandi +3
Autonomous navigation in unstructured off-road environments requires reasoning about both vehicle--terrain interaction and environmental unknowns. We propose a model-based framewor…
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…
Let-It-Flow: Simultaneous Optimization of 3D Flow and Object Clustering
Patrik Vacek, David Hurych, Tomáš Svoboda +1
We study the problem of self-supervised 3D scene flow estimation from real large-scale raw point cloud sequences, which is crucial to various tasks like trajectory prediction or in…
Regularizing Self-supervised 3D Scene Flows with Surface Awareness and Cyclic Consistency
Patrik Vacek, David Hurych, Karel Zimmermann +2
Learning without supervision how to predict 3D scene flows from point clouds is essential to many perception systems. We propose a novel learning framework for this task which impr…
T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds
Awet Haileslassie Gebrehiwot, David Hurych, Karel Zimmermann +2
Deep perception models have to reliably cope with an open-world setting of domain shifts induced by different geographic regions, sensor properties, mounting positions, and several…
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…