AgenticRoboRL 框架通过后训练提升机器人规划器性能
AgenticRoboRL: Bringing Coding-Agent Post-Training to Agentic Robotics
AgenticRoboRL 框架通过监督微调和多轮强化学习提升机器人规划器，在 LIBERO-PRO 上将 9B 参数规划器成功率从 18.1% 提升至 71.0% 不依赖外部记忆，接近 Codex (GPT-5.5) 使用外部记忆达成的 72.1%。该框架适配不同执行机制，将记录的规划器交互与任务结果连接，通过适配器实现通用强化学习训练流程。
Published on Oct 20
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Abstract
AgenticRoboRL improves robot planners via supervised fine-tuning and multi-turn reinforcement learning across diverse harnesses, significantly boosting success rates without external memory.
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.
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