近接性と安全性のバランスを取る：歩行者群衆中の人間追従のための制約分解
Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds
混雑環境での人間追従タスクを、疎なタスク報酬と独立したコスト制約に分解するマルチ制約強化学習手法を提案し、予測不確実性をコストに組み込むことで近接性と安全性のトレードオフを明示的に調整可能にした。
詳しい要約
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著者: Shiting Gong, Jianpeng Yao, Jinfeng Wang, Marco Pavone, Jiachen Li
分類: cs.RO, cs.AI, cs.LG, eess.SY
原文アブストラクト
Following a target human in crowded environments involves an inherent conflict between staying close to the target and navigating safely among surrounding pedestrians and obstacles. This conflict becomes more severe in dense scenarios, where aggressive following risks collisions and conservative margins lead to target loss, especially when pedestrian behaviors are unfamiliar or unpredictable. Existing reinforcement learning (RL) methods typically encode these competing objectives into a single dense reward, but the resulting proximity-safety balance is implicit and difficult to adjust across conditions. To address this, we decompose the human-following task into a sparse task reward and independent cost constraints within a multi-constraint RL formulation, where each constraint is managed through cost thresholds with direct behavioral meaning rather than implicit reward weight ratios, allowing explicit and tunable control over the trade-off. We further quantify the prediction uncertainty of human motions and integrate these estimates into the RL costs to enhance safety under unpredictable conditions. Extensive experiments across both in-distribution and out-of-distribution settings demonstrate that our method achieves an effective proximity-safety balance compared to baselines. Real-robot deployment further validates the feasibility of our method in real-world scenarios. More details are available on our project page: https://nav-ps-balance.github.io/.