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MIDGARD: A Robot Navigation Simulator for Outdoor Unstructured Environments

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European Robotics Forum 2024 (ERF 2024)

Abstract

We present MIDGARD, a simulation platform based on Unreal Engine for training autonomous robots in complex outdoor unstructured environments. It offers photorealistic 3D scenes, procedural scene generation, and integration with ROS and OpenAI Gym. The focus of MIDGARD is on navigation, where an autonomous agent travels from random initial positions to designated target locations avoiding obstacles, enabling researchers to develop and evaluate novel algorithms and navigation methods. We evaluate MIDGARD’s suitability as a research tool by training navigation algorithms based on reinforcement learning; we also assess sim-to-real transfer capabilities in a traversable horizon prediction task, using deep learning models on RGB images only.

MIDGARD builds and docs are available at www.midgardsim.org.

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Notes

  1. 1.

    “Kingdom of mankind”, i.e., Earth, in Norse mythology.

  2. 2.

    The agent has been trained for 2,500 steps with an update of 80 epochs at each step. The initial learning rate for the actor was 0.0003 and 0.001 for the critic.

References

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Acknowledgement

G. Muscato, C. Spampinato and F. Cancelliere acknowledge financial support from PNRR MUR project PE0000013-FAIR.

D. C. Guastella acknowledges support by the project PON R&I REACT-EU.

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Correspondence to Riccardo E. Sarpietro .

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Vecchio, G. et al. (2024). MIDGARD: A Robot Navigation Simulator for Outdoor Unstructured Environments. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-76424-0_22

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