绿色视觉语言动作模型：面向通用机器人的分阶段视觉-语言-动作架构

摘要

我们推出Green-VLA——一种分阶段实施的视觉-语言-动作框架，专为Green仿人机器人的实际部署设计，同时保持对不同形态机器人的泛化能力。该框架采用五阶段渐进式课程：（L0）基础视觉语言模型、（L1）多模态接地、（R0）多形态预训练、（R1）特定形态适配、（R2）强化学习策略对齐。我们通过时序对齐与质量过滤构建了可扩展的数据处理流水线（涵盖3000小时演示数据），并采用统一的多形态感知动作接口，使单一策略能同时控制仿人机器人、移动机械臂与固定基座机械臂。在推理阶段，该VLA控制器集成了任务进度预测、分布外检测和基于关节预测的引导机制，以提升安全性与目标选择精度。在Simpler BRIDGE WidowX与CALVIN ABC-D仿真环境及实体机器人上的实验表明，经过强化学习对齐的策略在成功率、鲁棒性和长周期任务效率方面均展现出卓越的泛化能力与性能提升。

English

We introduce Green-VLA, a staged Vision-Language-Action (VLA) framework for real-world deployment on the Green humanoid robot while maintaining generalization across diverse embodiments. Green-VLA follows a five stage curriculum: (L0) foundational VLMs, (L1) multimodal grounding, (R0) multi-embodiment pretraining, (R1) embodiment-specific adaptation, and (R2) reinforcement-learning (RL) policy alignment. We couple a scalable data-processing pipeline (3,000 hours of demonstrations) with temporal alignment and quality filtering, and use a unified, embodiment-aware action interface enabling a single policy to control humanoids, mobile manipulators, and fixed-base arms. At inference, the VLA controller is enhanced with episode-progress prediction, out-of-distribution detection, and joint-prediction-based guidance to improve safety and precise target selection. Experiments on Simpler BRIDGE WidowX and CALVIN ABC-D, as well as real-robot evaluations, show strong generalization and performance gains from RL alignment in success rate, robustness, and long-horizon efficiency.

绿色视觉语言动作模型：面向通用机器人的分阶段视觉-语言-动作架构

Green-VLA: Staged Vision-Language-Action Model for Generalist Robots

摘要

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