RoboRAN: 強化学習ベース自律ナビゲーションのための統合ロボティクスフレームワーク
RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation
陸・海・空・宇宙など多様なロボットプラットフォームで強化学習によるナビゲーション方策を訓練・評価・実機展開できる統一フレームワークを提案し、sim-to-real転移と横断的評価基盤を実現した。
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著者: Matteo El-Hariry, Antoine Richard, Ricard M. Castan, Luis F. W. Batista, Matthieu Geist, Cedric Pradalier, Miguel Olivares-Mendez
分類: cs.RO, cs.AI
原文アブストラクト
Autonomous robots must navigate and operate in diverse environments, from terrestrial and aquatic settings to aerial and space domains. While Reinforcement Learning (RL) has shown promise in training policies for specific autonomous robots, existing frameworks and benchmarks are often constrained to unique platforms, limiting generalization and fair comparisons across different mobility systems. In this paper, we present a multi-domain framework for training, evaluating and deploying RL-based navigation policies across diverse robotic platforms and operational environments. Our work presents four key contributions: (1) a scalable and modular framework, facilitating seamless robot-task interchangeability and reproducible training pipelines; (2) sim-to-real transfer demonstrated through real-world experiments with multiple robots, including a satellite robotic simulator, an unmanned surface vessel, and a wheeled ground vehicle; (3) the release of the first open-source API for deploying Isaac Lab-trained policies to real robots, enabling lightweight inference and rapid field validation; and (4) uniform tasks and metrics for cross-medium evaluation, through a unified evaluation testbed to assess performance of navigation tasks in diverse operational conditions (aquatic, terrestrial and space). By ensuring consistency between simulation and real-world deployment, RoboRAN lowers the barrier to developing adaptable RL-based navigation strategies. Its modular design enables straightforward integration of new robots and tasks through predefined templates, fostering reproducibility and extension to diverse domains. To support the community, we release RoboRAN as open-source.
