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Feel-WM is presented, the first off-road navigation world model that conditions on proprioception and predicts what the robot will feel alongside what the camera will see, and outperforms visual-only navigation world models in open-loop planning and closed-loop rough-terrain navigation across wheeled and legged platforms.
CanonNav consistently outperforms traditional RGB-D methods by disentangling navigation behavior from camera geometry, enabling safer and more effective visual navigation across platforms.
ORION transforms how visual representations are structured for navigation, leading to a significant boost in performance even in visually challenging environments.
FPAS achieves a breakthrough in navigation efficiency by dynamically adjusting sampling density based on environmental openness, outperforming traditional planners.
Adapting vision foundation models with task-specific prompts and geometric knowledge distillation dramatically improves traversability estimation in challenging outdoor environments, outperforming existing methods in both accuracy and reliability.
Style augmentation and texture regularization can significantly improve semantic segmentation robustness in off-road environments facing distribution shifts.
