自律移動ロボットのための実用的因果推論フレームワーク：シミュレーションから実世界の人間環境へ
A Practical Causal Framework for Autonomous Mobile Robots: From Simulation to Real-World Human Environments
シミュレーションで学習した因果構造を保ちつつ、少数の実観測でパラメータのみ更新することで、移動ロボットが人間共存環境へ迅速にsim2real転移し安全にナビゲーションできる枠組みを提案した。
著者: Luca Castri, Gloria Beraldo, Nicola Bellotto
分類: cs.RO, cs.AI
原文アブストラクト
Causal inference has emerged as a powerful paradigm enabling robots to make safe, efficient decisions by reasoning about the ``why'' behind events. However, its real-world application is hindered by the difficulty of collecting causal data, especially in human-populated environments where training takes time, while users expect immediate operation. To this end, we tackle the challenge of transferring simulation-learnt causal relations to the real world with minimal effort. While retaining the invariant high-level causal structure, we empirically demonstrate the system adapts efficiently to real-world situations by updating only the causal model parameters with a limited set of new observations. Our results show that the parameter update enables rapid sim-to-real transfer. The framework runs efficiently onboard of a mobile robot and significantly outperforms a non-causal baselines navigating successfully and safely in complex human-shared scenarios.