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About this paper
Hongbo Zhang, Zhongyu Li, Xuanqi Zeng, Laura Smith, Kyle Stachowicz, Dhruv Shah, Linzhu Yue, Zhitao Song and 4 more
The authors develop a vision-based planner that uses a quadruped’s locomotion performance to estimate terrain costs and steer around surfaces. In Gazebo sparse-obstacle tests, its combined value-based and tracking-error estimator yields 2.23 ± 0.52 seconds in simulated low-friction regions, versus 3.14 ± 0.66 seconds for tracking error alone. Real-world trials show route avoidance, but transfer to new terrain textures remains limited.
Key insights
- A robot-centric traversability cost can be effectively derived from the value function of a low-level locomotion controller, reflecting whole-body control performance and stability on varied terrains.
- Integrating both RGB (for terrain texture) and depth (for geometry and obstacles) vision is essential for a comprehensive understanding of complex outdoor environments, leading to improved navigation.
- Combining diverse offline datasets (human and robot demonstrations) with online real-world experience, through a framework like RLPD, enables sample-efficient training of complex vision-based policies on physical robots.
Summary by alphaXiv.
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