AgenticRoboRL: Improving Robot Planners with Coding-Agent Post-Training Methods

AgenticRoboRL proposes a framework for post-training robot planners across different robot execution harnesses. The training recipe combines supervised fine-tuning with multi-turn reinforcement learning, and harness-specific adapters connect planner interaction records and task outcomes to a unified RL training pipeline while keeping execution components fixed.