# STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation; Results from the DARPA Subterranean Challenge

arXiv: https://arxiv.org/abs/2303.01614 · HTML: https://mlsanity.com/paper/2303.01614/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2023.

## Built on by (most cited first, 12 of 12)

- [RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving](https://mlsanity.com/paper/2402.19341/) (2024)
- [Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End](https://mlsanity.com/paper/2310.03581/) (2023)
- [DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation](https://mlsanity.com/paper/2403.09900/) (2024)
- [Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation](https://mlsanity.com/paper/2401.17484/) (2024)
- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [QuadrupedGPT: Towards a Versatile Quadruped Agent in Open-ended Worlds](https://mlsanity.com/paper/2406.16578/) (2024)
- [Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping](https://mlsanity.com/paper/2403.11876/) (2024)
- [Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option](https://mlsanity.com/paper/2506.17601/) (2025)
- [TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation](https://mlsanity.com/paper/2404.15256/) (2024)
- [FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud](https://mlsanity.com/paper/2406.16564/) (2024)
- [Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian Process](https://mlsanity.com/paper/2503.04134/) (2025)
- [Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy](https://mlsanity.com/paper/2605.15517/) (2026)

## Abstract (arXiv)

Although autonomy has gained widespread usage in structured and controlled environments, robotic autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, rubble, and other post-disaster sites pose unique and challenging problems for autonomous navigation. Based on our participation in the DARPA Subterranean Challenge, we propose an approach to improve autonomous traversal of robots in subterranean environments that are perceptually degraded and completely unknown through a traversability and planning framework called STEP (Stochastic Traversability Evaluation and Planning). We present 1) rapid uncertainty-aware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC), 4) fast recovery behaviors to account for unexpected scenarios that may cause failure, and 5) risk-based gait adaptation for quadrupedal robots. We illustrate and validate extensive results from our experiments on wheeled and legged robotic platforms in field studies at the Valentine Cave, CA (cave environment), Kentucky Underground, KY (mine environment), and Louisville Mega Cavern, KY (final competition site for the DARPA Subterranean Challenge with tunnel, urban, and cave environments).