安全な地図なしナビゲーションのためのカリキュラム学習
Curriculum Learning for Safe Mapless Navigation
カリキュラム学習と転移学習を組み合わせ、地図なしロボットナビゲーションの安全性を標準的なend-to-end学習と比較検証した論文。未見環境での衝突を10%削減し、形式検証でも有効性を確認した。
著者: Luca Marzari, Davide Corsi, Enrico Marchesini, Alessandro Farinelli
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
This work investigates the effects of Curriculum Learning (CL)-based approaches on the agent's performance. In particular, we focus on the safety aspect of robotic mapless navigation, comparing over a standard end-to-end (E2E) training strategy. To this end, we present a CL approach that leverages Transfer of Learning (ToL) and fine-tuning in a Unity-based simulation with the Robotnik Kairos as a robotic agent. For a fair comparison, our evaluation considers an equal computational demand for every learning approach (i.e., the same number of interactions and difficulty of the environments) and confirms that our CL-based method that uses ToL outperforms the E2E methodology. In particular, we improve the average success rate and the safety of the trained policy, resulting in 10% fewer collisions in unseen testing scenarios. To further confirm these results, we employ a formal verification tool to quantify the number of correct behaviors of Reinforcement Learning policies over desired specifications.
