Adaptive Policy Switching of Two-Wheeled Differential Robots for Traversing over Diverse Terrains
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910041688244224 |
|---|---|
| author | Izawa, Haruki Takai, Takeshi Kitano, Shingo Miyaguchi, Mikita Kawashima, Hiroaki |
| author_facet | Izawa, Haruki Takai, Takeshi Kitano, Shingo Miyaguchi, Mikita Kawashima, Hiroaki |
| contents | Exploring lunar lava tubes requires robots to traverse without human intervention. Because pre-trained policies cannot fully cover all possible terrain conditions, our goal is to enable adaptive policy switching, where the robot selects an appropriate terrain-specialized model based on its current terrain features. This study investigates whether terrain types can be estimated effectively using posture-related observations collected during navigation. We fine-tuned a pre-trained policy using Proximal Policy Optimization (PPO), and then collected the robot's 3D orientation data as it moved across flat and rough terrain in a simulated lava-tube environment. Our analysis revealed that the standard deviation of the robot's pitch data shows a clear difference between these two terrain types. Using Gaussian mixture models (GMM), we evaluated terrain classification across various window sizes. An accuracy of more than 98% was achieved when using a 70-step window. The result suggests that short-term orientation data are sufficient for reliable terrain estimation, providing a foundation for adaptive policy switching. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04761 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Policy Switching of Two-Wheeled Differential Robots for Traversing over Diverse Terrains Izawa, Haruki Takai, Takeshi Kitano, Shingo Miyaguchi, Mikita Kawashima, Hiroaki Robotics Systems and Control Exploring lunar lava tubes requires robots to traverse without human intervention. Because pre-trained policies cannot fully cover all possible terrain conditions, our goal is to enable adaptive policy switching, where the robot selects an appropriate terrain-specialized model based on its current terrain features. This study investigates whether terrain types can be estimated effectively using posture-related observations collected during navigation. We fine-tuned a pre-trained policy using Proximal Policy Optimization (PPO), and then collected the robot's 3D orientation data as it moved across flat and rough terrain in a simulated lava-tube environment. Our analysis revealed that the standard deviation of the robot's pitch data shows a clear difference between these two terrain types. Using Gaussian mixture models (GMM), we evaluated terrain classification across various window sizes. An accuracy of more than 98% was achieved when using a 70-step window. The result suggests that short-term orientation data are sufficient for reliable terrain estimation, providing a foundation for adaptive policy switching. |
| title | Adaptive Policy Switching of Two-Wheeled Differential Robots for Traversing over Diverse Terrains |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2603.04761 |