Off-road robots feel terrain to improve navigation and avoid failure
Feeling Terrain Before Crossing: World Models for Off-Road Navigation
RoboticsComputer Vision and Pattern Recognition
Summary
Robots moving through rough, natural places like forests and mountains need to predict not just what they will see but also how the ground will affect them, such as slipping or shaking. The authors created a new navigation model called Feel-WM that uses the robot's internal sensors about movement and balance to better forecast these physical effects. This helps the robot plan safer routes and avoid trouble before it happens, making off-road travel more reliable. Their tests show that Feel-WM outperforms previous visual-only systems and works on different types of robots.
What this means in practice
- •For autonomous vehicle engineers: Improve off-road autonomous navigation by incorporating future physical dynamics to reduce failure on rough terrain.
- •For robotics software developers: Develop onboard path planners that predict both sensory and proprioceptive outcomes for better navigation of unstructured environments.
Authors
E-In Son, Dong-Wook Kim, Ji-Hoon Hwang, Kangsun Lee, Jisung Bae, Jung-Taak Kim, Seung-Woo Seo
Abstract
Navigation world models plan by foresight, predicting the future that each candidate action sequence produces and selecting the best, rather than mapping observations to actions directly. Unlike urban settings where a predicted scene is a sufficient proxy, off-road navigation hinges on the robot--terrain interaction, so the prediction must cover not only what the camera will see but what the robot will feel. However, existing scene-focused models do not predict how much the robot will slip, tilt or shake along a planned trajectory. Proprioception captures these dynamics directly and, when used as input, improves the prediction of the physical future. We present Feel-WM, the first off-road navigation world model that conditions on proprioception and predicts what the robot will feel alongside what the camera will see. The physical future takes the form of a future proprioceptive state and a failure risk, both learned from the robot's own experience without human labels. The planner rolls out the physical future alongside the scene and weighs the predicted failure risk against goal similarity in a separable score. Experiments on real off-road data and in simulation demonstrate that Feel-WM outperforms visual-only navigation world models in open-loop planning and closed-loop rough-terrain navigation across wheeled and legged platforms. Deployed on a Husky on mountain trails, Feel-WM plans onboard, predicts rough ground ahead and steers around it, completing courses that an end-to-end policy fails.