MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models

• URL: http://arxiv.org/abs/2503.08007v1
• Date: Tue, 11 Mar 2025 03:13:45 GMT
• Title: MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models
• Authors: Han Zhao, Wenxuan Song, Donglin Wang, Xinyang Tong, Pengxiang Ding, Xuelian Cheng, Zongyuan Ge,
• Abstract summary: This paper introduces a novel vision--action (VLA) model, mixture of robotic experts (MoRE) for quadruped robots.<n>MoRE integrates multiple low-rank adaptation modules as distinct experts within a dense multi-modal large language model.<n>Experiments demonstrate that MoRE outperforms all baselines across six different skills and exhibits superior generalization capabilities in out-of-distribution scenarios.
• Score: 34.138699712315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing versatile quadruped robots that can smoothly perform various actions and tasks in real-world environments remains a significant challenge. This paper introduces a novel vision-language-action (VLA) model, mixture of robotic experts (MoRE), for quadruped robots that aim to introduce reinforcement learning (RL) for fine-tuning large-scale VLA models with a large amount of mixed-quality data. MoRE integrates multiple low-rank adaptation modules as distinct experts within a dense multi-modal large language model (MLLM), forming a sparse-activated mixture-of-experts model. This design enables the model to effectively adapt to a wide array of downstream tasks. Moreover, we employ a reinforcement learning-based training objective to train our model as a Q-function after deeply exploring the structural properties of our tasks. Effective learning from automatically collected mixed-quality data enhances data efficiency and model performance. Extensive experiments demonstrate that MoRE outperforms all baselines across six different skills and exhibits superior generalization capabilities in out-of-distribution scenarios. We further validate our method in real-world scenarios, confirming the practicality of our approach and laying a solid foundation for future research on multi-task learning in quadruped robots.

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