RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

RoboGPT-R1 is introduced as a two-stage fine-tuning framework aimed at enhancing robot task planning through reinforcement learning (RL) and supervised training. It utilizes the Qwen2.5-VL-3B model and demonstrates a 21.33% improvement over GPT-4o-mini and a 20.33% increase over Qwen2.5-VL-7B on the EmbodiedBench benchmark, addressing challenges in long-horizon manipulation tasks. This framework is significant for practitioners as it combines RL with a rule-based reward function to improve visual-spatial reasoning and action sequence consistency in real-world robotic applications.