Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics

Large Vision-Language Models (LVLMs) have recently shown great promise in advancing robotics by combining embodied reasoning with robot control. A common approach involves training on embodied reasoning tasks related to robot control using Supervised Fine-Tuning (SFT). However, SFT datasets are often heuristically constructed and not explicitly optimized for improving robot control. Furthermore, SFT often leads to issues such as catastrophic forgetting and reduced generalization performance. To address these limitations, we introduce ROBOT-R1, a novel framework that leverages reinforcement learning to enhance embodied reasoning specifically for robot control.