Know When to Escalate: Critic-Guided Adaptive Execution for Cloud-Local Embodied Agents

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%.