VisFly: An Efficient and Versatile Simulator for Training Vision-based Flight
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909308363472896 |
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| author | Li, Fanxing Sun, Fangyu Zhang, Tianbao Zou, Danping |
| author_facet | Li, Fanxing Sun, Fangyu Zhang, Tianbao Zou, Danping |
| contents | We present VisFly, a quadrotor simulator designed to efficiently train vision-based flight policies using reinforcement learning algorithms. VisFly offers a user-friendly framework and interfaces, leveraging Habitat-Sim's rendering engines to achieve frame rates exceeding 10,000 frames per second for rendering motion and sensor data. The simulator incorporates differentiable physics and is seamlessly wrapped with the Gym environment, facilitating the straightforward implementation of various learning algorithms. It supports the directly importing open-source scene datasets compatible with Habitat-Sim, enabling training on diverse real-world environments simultaneously. To validate our simulator, we also make three reinforcement learning examples for typical flight tasks relying on visual observations. The simulator is now available at [https://github.com/SJTU-ViSYS-team/VisFly]. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14783 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VisFly: An Efficient and Versatile Simulator for Training Vision-based Flight Li, Fanxing Sun, Fangyu Zhang, Tianbao Zou, Danping Robotics We present VisFly, a quadrotor simulator designed to efficiently train vision-based flight policies using reinforcement learning algorithms. VisFly offers a user-friendly framework and interfaces, leveraging Habitat-Sim's rendering engines to achieve frame rates exceeding 10,000 frames per second for rendering motion and sensor data. The simulator incorporates differentiable physics and is seamlessly wrapped with the Gym environment, facilitating the straightforward implementation of various learning algorithms. It supports the directly importing open-source scene datasets compatible with Habitat-Sim, enabling training on diverse real-world environments simultaneously. To validate our simulator, we also make three reinforcement learning examples for typical flight tasks relying on visual observations. The simulator is now available at [https://github.com/SJTU-ViSYS-team/VisFly]. |
| title | VisFly: An Efficient and Versatile Simulator for Training Vision-based Flight |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.14783 |