Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments
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Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential of the robots' locomotion capabilities while operating within safety limits under uncertainty. The robot must sense and analyze the traversability of the surrounding terrain, which depends on the hardware, locomotion control, and terrain properties. It may contain information about the risk, energy, or time consumption needed to traverse the terrain. To avoid hand-crafted traversability cost functions we propose to collect traversability information about the robot and locomotion policy by simulating the traversal over randomly generated terrains using a physics simulator. Thousand of robots are simulated in parallel controlled by the same locomotion policy used in reality to acquire 57 years of real-world locomotion experience equivalent. For deployment on the real robot, a sparse convolutional network is trained to predict the simulated traversability cost, which is tailored to the deployed locomotion policy, from an entirely geometric representation of the environment in the form of a 3D voxel-occupancy map. This representation avoids the need for commonly used elevation maps, which are error-prone in the presence of overhanging obstacles and multi-floor or low-ceiling scenarios. The effectiveness of the proposed traversability prediction network is demonstrated for path planning for the legged robot ANYmal in various indoor and natural environments.
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Publication status
published
Editor
Book title
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
5722 - 5729
Publisher
IEEE
Event
35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Legged Robotics; Navigation; Traversability
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems
Notes
Funding
166232 - Data-driven control approaches for advanced legged locomotion (SNF)
188596 - Perceptive Dynamic Locomotion on Rough Terrain (SNF)
780883 - subTerranean Haptic INvestiGator (EC)
188596 - Perceptive Dynamic Locomotion on Rough Terrain (SNF)
780883 - subTerranean Haptic INvestiGator (EC)