让视觉语言动作模型在线高效自适应，提升机器人在新环境中的学习速度与泛化能力。
Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models
- 通过动态奖励生成与分步目标分解，实现任务自适应的课程学习。
- 1次示范下性能提升28.5%，长任务成功率提高12.3%，跨任务迁移率达31.2%。
- 适合需要快速适应新任务的机器人部署场景，尤其适用于少样本与动态环境。
视觉-语言-动作（VLA）模型通过利用预训练的视觉语言表征，在机器人操作中展现出巨大潜力。然而，现有VLA训练方法存在两大瓶颈：对新环境泛化能力差，且训练效率低，依赖大量演示数据。本文提出Agentic-VLA，一种代理式训练框架，使VLA能通过三项关键创新实现高效在线自适应：(1) 自适应奖励生成，根据模型当前能力与任务复杂度动态调整奖励函数，将复杂任务分解为可学习的子目标以支持课程学习；(2) 语言引导探索，由评论模型提供结构化指导，替代随机采样，实现系统性探索；(3) 经验记忆机制，存储并检索任务相关策略权重，用于相似任务的热启动适应。我们在LIBERO基准上评估该框架，结果显示：长时序任务成功率提升12.3%，1次示范学习性能提升28.5%，跨任务迁移率从0%提升至31.2%且无需特定任务演示。此外，收敛速度比现有方法快2.4倍。该框架在双臂机器人平台RoboTwin 2.0（含随机化的Hard设置）上也表现出色。结果表明，Agentic-VLA是实现真正持续学习的VLA系统的重要进展。
原文摘要 · Abstract (English)
Vision-Language-Action (VLA) models have emerged as a promising paradigm for robotic manipulation by leveraging pre-trained vision-language representations. However, current VLA training methods suffer from two critical limitations: poor generalization to novel environments and low training efficiency requiring extensive demonstrations. We introduce Agentic-VLA, an agentic training framework that enables VLAs to efficiently adapt online through three key innovations: (1) Adaptive Reward Synthesis, which dynamically generates and adjusts reward functions based on the VLA's current capabilities and task complexity, decomposing complex tasks into learnable sub-goals for curriculum learning; (2) Language-Guided Exploration, where a critic model provides structured guidance for systematic exploration rather than random sampling; and (3) Experience Memory,which stores and retrieves task-relevant policy weights for warm-starting adaptation to similar tasks. We evaluate Agentic-VLA on the LIBERO benchmark, achieving substantial improvements: +12.3% on long-horizon tasks, +28.5% in 1-shot learning, and enabling cross-task transfer from 0% to 31.2% without task-specific demonstrations. Our framework also demonstrates 2.4x faster convergence compared to existing online adaptation methods. Beyond LIBERO, Agentic-VLA retains its advantage on the dual-arm RoboTwin 2.0 benchmark, including under its randomized Hard setting. These results establish Agentic-VLA as a significant step toward truly adaptive VLA systems capable of continuous learning in deployment.
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