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

ROBOT-R1 is a novel framework that enhances embodied reasoning in robotics by integrating reinforcement learning (RL) with large vision-language models (LVLMs). Traditional methods often rely on supervised fine-tuning (SFT), which can be limited by heuristically constructed datasets and problems with catastrophic forgetting. In contrast, ROBOT-R1 reformulates robotic control as a multiple-choice question-answering task, where models are trained to predict next-state keypoints while generating explicit reasoning traces that are optimized via reinforcement learning. Experimental results demonstrate that ROBOT-R1 significantly outperforms SFT-based baselines across various benchmarks, including robot manipulation and spatial reasoning. Even with a compact 7B parameter model, ROBOT-R1 surpasses commercial models like GPT-4o on low-level action control tasks. The framework demonstrates robust transferability to real-world robotic environments and downstream tasks, indicating that the embodied reasoning learned through RL is generalizable rather than task-specific. Through detailed ablation studies, the researchers show that the performance improvements stem from the structured reasoning traces and the auxiliary training tasks, such as current state estimation and movement prediction. The evolution of reasoning during the training process shows that the model transitions from verbose, summary-style reasoning to concise, logical traces. The researchers conclude that ROBOT-R1 is a scalable, efficient solution that makes advanced robotic control accessible to smaller research labs and companies.

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. ROBOT-R1 learns to predict the next keypoint state required for task completion, conditioned on the current scene image and environment metadata derived from expert demonstrations. Inspired by the DeepSeek-R1 learning approach, ROBOT-R1 samples reasoning-based responses and reinforces those that lead to more accurate predictions. To rigorously evaluate ROBOT-R1, we also introduce a new benchmark that demands the diverse embodied reasoning capabilities for the task. Our experiments show that models trained with Robot-R1 outperform SFT methods on embodied reasoning tasks. Despite having only 7B parameters, ROBOT-R1 even surpasses GPT-4o on reasoning tasks related to low-level action control, such as spatial and movement reasoning.

The discussion focuses on the transition from broad planning to concise, structured reasoning during RL training. The authors acknowledge that while ROBOT-R1 improves embodied reasoning, limitations exist, such as the current focus on Cartesian XYZ positions without end-effector orientation or gripper control. Future directions include incorporating these additional state dimensions and designing complementary rewards to minimize the risks of unintended robotic actions during reinforcement learning.