TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation

Junli Ren, Yikai Liu, Yingru Dai, Junfeng Long, Guijin Wang
Proceedings of The 8th Conference on Robot Learning, PMLR 270:3454-3473, 2025.

Abstract

Legged navigation is typically examined within open-world, off-road, and challenging environments. In these scenarios, estimating external disturbances requires a complex synthesis of multi-modal information. This underlines a major limitation in existing works that primarily focus on avoiding obstacles. In this work, we propose TOP-Nav, a novel legged navigation framework that integrates a comprehensive path planner with Terrain awareness, Obstacle avoidance and close-loop Proprioception. TOP-Nav underscores the synergies between vision and proprioception in both path and motion planning. Within the path planner, we present a terrain estimator that enables the robot to select waypoints on terrains with higher traversability while effectively avoiding obstacles. In the motion planning level, we construct a proprioception advisor from the learning-based locomotion controller to provide motion evaluations for the path planner. Based on the close-loop motion feedback, we offer online corrections for the vision-based terrain and obstacle estimations. Consequently, TOP-Nav achieves open-world navigation that the robot can handle terrains or disturbances beyond the distribution of prior knowledge and overcomes constraints imposed by visual conditions. Building upon extensive experiments conducted in both simulation and real-world environments, TOP-Nav demonstrates superior performance in open-world navigation compared to existing methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v270-ren25a, title = {TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation}, author = {Ren, Junli and Liu, Yikai and Dai, Yingru and Long, Junfeng and Wang, Guijin}, booktitle = {Proceedings of The 8th Conference on Robot Learning}, pages = {3454--3473}, year = {2025}, editor = {Agrawal, Pulkit and Kroemer, Oliver and Burgard, Wolfram}, volume = {270}, series = {Proceedings of Machine Learning Research}, month = {06--09 Nov}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v270/main/assets/ren25a/ren25a.pdf}, url = {https://proceedings.mlr.press/v270/ren25a.html}, abstract = {Legged navigation is typically examined within open-world, off-road, and challenging environments. In these scenarios, estimating external disturbances requires a complex synthesis of multi-modal information. This underlines a major limitation in existing works that primarily focus on avoiding obstacles. In this work, we propose TOP-Nav, a novel legged navigation framework that integrates a comprehensive path planner with Terrain awareness, Obstacle avoidance and close-loop Proprioception. TOP-Nav underscores the synergies between vision and proprioception in both path and motion planning. Within the path planner, we present a terrain estimator that enables the robot to select waypoints on terrains with higher traversability while effectively avoiding obstacles. In the motion planning level, we construct a proprioception advisor from the learning-based locomotion controller to provide motion evaluations for the path planner. Based on the close-loop motion feedback, we offer online corrections for the vision-based terrain and obstacle estimations. Consequently, TOP-Nav achieves open-world navigation that the robot can handle terrains or disturbances beyond the distribution of prior knowledge and overcomes constraints imposed by visual conditions. Building upon extensive experiments conducted in both simulation and real-world environments, TOP-Nav demonstrates superior performance in open-world navigation compared to existing methods.} }
Endnote
%0 Conference Paper %T TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation %A Junli Ren %A Yikai Liu %A Yingru Dai %A Junfeng Long %A Guijin Wang %B Proceedings of The 8th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Pulkit Agrawal %E Oliver Kroemer %E Wolfram Burgard %F pmlr-v270-ren25a %I PMLR %P 3454--3473 %U https://proceedings.mlr.press/v270/ren25a.html %V 270 %X Legged navigation is typically examined within open-world, off-road, and challenging environments. In these scenarios, estimating external disturbances requires a complex synthesis of multi-modal information. This underlines a major limitation in existing works that primarily focus on avoiding obstacles. In this work, we propose TOP-Nav, a novel legged navigation framework that integrates a comprehensive path planner with Terrain awareness, Obstacle avoidance and close-loop Proprioception. TOP-Nav underscores the synergies between vision and proprioception in both path and motion planning. Within the path planner, we present a terrain estimator that enables the robot to select waypoints on terrains with higher traversability while effectively avoiding obstacles. In the motion planning level, we construct a proprioception advisor from the learning-based locomotion controller to provide motion evaluations for the path planner. Based on the close-loop motion feedback, we offer online corrections for the vision-based terrain and obstacle estimations. Consequently, TOP-Nav achieves open-world navigation that the robot can handle terrains or disturbances beyond the distribution of prior knowledge and overcomes constraints imposed by visual conditions. Building upon extensive experiments conducted in both simulation and real-world environments, TOP-Nav demonstrates superior performance in open-world navigation compared to existing methods.
APA
Ren, J., Liu, Y., Dai, Y., Long, J. & Wang, G.. (2025). TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation. Proceedings of The 8th Conference on Robot Learning, in Proceedings of Machine Learning Research 270:3454-3473 Available from https://proceedings.mlr.press/v270/ren25a.html.

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