Reasoning-based robotic policies using large language and vision-language models achieve strong semantic planning capabilities but mostly suffer from a high inference latency that limits practical real-time deployment. In this work, we observe that robotic reasoning workloads contain substantial temporal redundancy, where consecutive observations frequently produce identical actions and subgoals. Based on this insight, we present REIS, a human cognition inspired robotic decision-making framework that minimizes unnecessary reasoning while preserving semantic adaptability. REIS combines lightweight scene gating, KV-steered affordance routing, and deliberative reasoning to accelerate robotic control under embodied constraints. Experiments on ALFRED, and real-world robotic tasks demonstrate that REIS significantly suppresses reasoning overhead while maintaining competitive task performance.

翻译：基于推理的机器人策略利用大型语言和视觉语言模型实现了强大的语义规划能力，但大多受限于高推理延迟，这限制了其在实际中的实时部署。在这项工作中，我们观察到机器人推理工作负载包含大量时间冗余，即连续的观察结果经常产生相同的动作和子目标。基于这一认识，我们提出了REIS，一种受人类认知启发的机器人决策框架，该框架在保持语义适应性的同时最小化不必要的推理。REIS结合了轻量级场景门控、键值驱动的可负担路由和深思熟虑的推理，以在具身约束下加速机器人控制。在ALFRED和真实世界机器人任务上的实验表明，REIS显著抑制了推理开销，同时保持了具有竞争力的任务性能。