机器人规划器也能被训练了：9B小模型追平GPT-5.5
机器人干活一直有个尴尬：负责「想下一步做什么」的规划器，用的是闭源大模型，而大模型本身并不会因为你的任务变好。这篇把训练编码智能体的那套方法搬到了机器人上：先用监督微调打底，再用多轮强化学习让规划器在真实任务里自己试错，而且通过适配器兼容了两种执行方式——调用VLA技能和生成控制代码。结果是一个90亿参数的开源规划器，成功率从18.1%飙到71.0%，没开外部记忆，逼近用着外部记忆的GPT-5.5(72.1%)。它不是你明天能用的东西，但信号很明确：机器人规划器不再只能靠闭源大模型，开源小模型也能被训练到同一水平，这会让整个机器人AI栈更开放、更便宜。
📄 原文摘要(英文)
Agentic robotics systems combine planners with robot harnesses to perform complex manipulation tasks. Recent work improves these systems through better harnesses and external memory, but relies on powerful closed-source models as planners and does not improve the planners' own capabilities. In contrast, coding agents have improved substantially by post-training the model itself. We therefore investigate whether post-training can also improve robot planners. However, applying it across robot harnesses requires accommodating different execution mechanisms, including VLA skill invocation and control-code generation. To this end, we introduce AgenticRoboRL, a framework for post-training robot planners across different harnesses. The training recipe combines supervised fine-tuning with multi-turn reinforcement learning. To accommodate different execution mechanisms, harness-specific adapters connect recorded planner interactions with task outcomes, enabling a common RL training pipeline while keeping execution components fixed. On LIBERO-PRO, AgenticRoboRL raises the success rate of a 9B-parameter planner from 18.1% to 71.0% without external memory, approaching the 72.1% achieved by Codex (GPT-5.5) with external memory. These results demonstrate post-training as an effective approach for improving robot planners. We will release the framework and trained planner checkpoints.