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Not AvailablNatural environments pose significant challenges for autonomous robot navigation, particularly due to their unstructured and ever-changing nature. Hiking trails, with their dynamic conditions influenced by weather, vegetation, and human traffic, represent one of these challenges. This work introduces a novel approach to autonomous hiking trail navigation that balances trail adherence with the flexibility to adapt to off-trail routes when necessary. The solution is a Traversability Analysis module that integrates semantic data from camera images with geometric information from LiDAR to create a comprehensive understanding of the surrounding terrain. A planner uses this traversability map to navigate safely, adhering to trails while allowing off-trail movement when necessary to avoid on-trail hazards or for safe off-trail shortcuts. The method is evaluated through simulation to determine the balance between semantic and geometric information in traversability estimation. These simulations tested various weights to assess their impact on navigation performance across different trail scenarios. Weights were then validated through autonomous field tests at the West Virginia University Core Arboretum, demonstrating the method’s effectiveness in a real-world environment.e
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Let Humanoids Hike! Integrative Skill Development over Complex Trails
Hiking on complex trails demands balance, agility, and adaptive decision-making over unpredictable terrain. Current humanoid research remains fragmented and inadequate for hiking: locomotion focuses on motor skills without long-term goals or situational awareness, while semantic navigation overlooks real-world embodiment and local terrain variability. We propose training humanoids to hike on complex trails, driving integrative skill development across visual perception, decision making, and motor execution. We develop a learning framework, LEGO-H, that enables a vision-equipped humanoid robot to hike complex trails autonomously. We introduce two technical innovations: {\bf 1)} A temporal vision transformer variant anticipates future local goals to guide movement, seamlessly integrating locomotion with goal-directed navigation. {\bf 2)} Latent representations of joint movement patterns, combined with hierarchical metric learning, enable smooth policy transfer from privileged training to onboard execution. These components allow LEGO-H to handle diverse physical and environmental challenges without relying on predefined motion patterns. Experiments across varied simulated trails and robot morphologies highlight LEGO-H's versatility and robustness, positioning hiking as a compelling testbed for embodied autonomy and LEGO-H as a baseline for future humanoid development.
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- PAR ID:
- 10677863
- Publisher / Repository:
- IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Date Published:
- Subject(s) / Keyword(s):
- humanoid locomotion, autonomous navigation, semantic navigation, hiking
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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