即時リセットは本当に必要か？衝突処理を見直して効率的なロボットナビゲーションを実現
Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation
衝突時に毎回環境をリセットする従来の強化学習手法を改め、同一エピソード内で再試行を許す「Multi-Collision reset Budget」を提案し、学習効率と実機性能を向上させた。
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著者: Shanze Wang, Xinming Zhang, Siwei Cheng, Xianghui Wang, Changwen Chen, Hailong Huang, Wei Zhang
分類: cs.RO
原文アブストラクト
Should a single collision necessarily terminate an entire navigation episode? In most deep reinforcement learning (DRL) frameworks for robot navigation, this remains the standard practice: every collision immediately triggers a global environment reset and is penalized as a complete task failure. While a collision during deployment naturally indicates task failure, applying the same treatment during training prevents the agent from exploring challenging obstacle configurations, which slows learning progress in the early training phase. In this work, we challenge this convention and propose a Multi-Collision reset Budget (MCB) framework that decouples local collision termination from global environment resets, allowing the agent to retry difficult configurations within the same episode. Simulation experiments show that MCB improves early-stage learning efficiency by reaching target success-rate levels with fewer interactions, with small collision budgets producing the most consistent gains. Real-world experiments on heterogeneous robot platforms further validate the deployability of the learned policies in cluttered environments.
