让视觉语言动作模型更稳定可靠，通过多样化经验训练提升真实机器人任务表现
Scaling by Diversified Experience for Vision-Language-Action Models
- 分离意图与控制特征，避免高阶推理干扰低阶操作
- 采用相似样本引导强化学习，显著减少策略更新波动
- 在真实机器人任务中成功率更高，适合部署于复杂场景
视觉-语言-动作（VLA）模型在实际部署中面临高阶推理与低阶控制耦合、策略优化不稳定的挑战。本文提出SyVLA，一种基于多样化经验训练的鲁棒VLA模型。通过引入意图解耦算法，将控制相关特征从推理上下文中分离；设计相似样本引导的强化学习流程，稳定策略更新并缓解分布偏移问题。在真实机器人任务及多模态基准上的大量实验表明，SyVLA相比现有方法实现了更高的任务成功率和更强的分布外泛化能力，同时有效保留了核心视觉-语言能力。代码与数据集已公开于项目主页。
原文摘要 · Abstract (English)
Vision-Language-Action models face significant challenges in real-world deployment due to the entanglement of high-level reasoning with low-level control, and the instability of policy optimization. In this paper, we introduce SyVLA, a robust VLA model trained with diversified experiences. We propose an Intention Decoupling algorithm to isolate control-relevant features from reasoning contexts and a similar-sample guided RL pipeline to stabilize policy updates and mitigate distribution shift. Extensive experiments on real-world robotic tasks and multi-modal benchmarks demonstrate that SyVLA achieves superior task success rates and stronger out-of-distribution generalization compared to existing methods, while effectively preserving core vision-language capabilities. Codes and Datasets is released on \href{https://sy-vla.github.io/}{project page}.
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