STaR: 長期的マルチモーダルロボット記憶のためのスケーラブルなタスク条件付き検索
STaR: Scalable Task-Conditioned Retrieval for Long-Horizon Multimodal Robot Memory
ロボットの長期記憶を構築し、情報ボトルネック原理に基づくタスク条件付き検索で必要な記憶を抽出するエージェント推論フレームワークを提案。屋内・屋外の実環境で有効性を実証。
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著者: Mingfeng Yuan, Hao Zhang, Mahan Mohammadi, Runhao Li, Jinjun Shan, Steven L. Waslander
分類: cs.RO, cs.AI
原文アブストラクト
Mobile robots are often deployed over long durations in diverse open, dynamic scenes, including indoor setting such as warehouses and manufacturing facilities, and outdoor settings such as agricultural and roadway operations. A core challenge is to build a scalable long-horizon memory that supports an agentic workflow for planning, retrieval, and reasoning over open-ended instructions at variable granularity, while producing precise, actionable answers for navigation. We present STaR, an agentic reasoning framework that (i) constructs a task-agnostic, multimodal long-term memory that generalizes to unseen queries while preserving fine-grained environmental semantics (object attributes, spatial relations, and dynamic events), and (ii) introduces a Scalable Task Conditioned Retrieval algorithm based on the Information Bottleneck principle to extract from long-term memory a compact, non-redundant, information-rich set of candidate memories for contextual reasoning. We evaluate STaR on NaVQA (mixed indoor/outdoor campus scenes) and WH-VQA, a customized warehouse benchmark with many visually similar objects built with Isaac Sim, emphasizing contextual reasoning. Across the two datasets, STaR consistently outperforms strong baselines, achieving higher success rates and markedly lower spatial error. We further deploy STaR on a real Husky wheeled robot in both indoor and outdoor environments, demonstrating robust long horizon reasoning, scalability, and practical utility. Project Website: https://trailab.github.io/STaR-website/
