WayFAST: Navigation With Predictive Traversability in the Field

Mateus V. Gasparino, Arun N. Sivakumar, Yixiao Liu, Andres E.B. Velasquez, Vitor A.H. Higuti, John Rogers, Huy Tran, Girish Chowdhary

Research output: Contribution to journalArticlepeer-review


We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.

Original languageEnglish (US)
Pages (from-to)10651-10658
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - Oct 1 2022


  • Field robots
  • semantic scene understanding
  • vision-based navigation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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