いつエスカレーションすべきか：批評家誘導によるクラウド・ローカル身体性エージェントの適応実行
Know When to Escalate: Critic-Guided Adaptive Execution for Cloud-Local Embodied Agents
軽量ローカルモデルが実行を担い、信頼性が低下した時だけ批評家モデルがクラウドの強力モデルへ制御を引き継ぐ適応実行フレームワークCAREを提案。共有ハンドオフメモリで文脈を保ち、成功率向上と遅延削減を両立した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Wei Zhang, Lirong Xiang, Charles Fleming
分類: cs.RO, cs.DC
原文アブストラクト
Recent advances in foundation-model agents have enabled increasingly capable embodied systems for long-horizon planning and interaction. However, embodied agents require fast, closed-loop decisions as they continuously perceive and act on a changing environment. Deploying strong cloud agents leaves the platform idle for seconds between consecutive actions, while lightweight local models respond quickly but are more vulnerable to errors. Existing hierarchical architectures typically assign models fixed roles, whereas conventional model routers make stateless per-request decisions. We present CARE, a critic-guided adaptive execution framework that dynamically determines when to escalate control from a local executor to a stronger cloud model. A lightweight critic model monitors execution reliability and triggers escalation when local execution becomes unreliable; once escalated, control persists until the agent recovers, avoiding step-wise switching. To support such control transfer, a shared handoff memory maintains execution context and task-level progress across executors, enabling the model to intervene without restarting from scratch. Across diverse embodied benchmarks, CARE improves task success by 6.3-18.3 percentage points over the strongest evaluated collaboration baseline. CARE further approaches or exceeds Cloud-only success while reducing median time-between-action latency by 28.8-82.6%.