AtomicVLA：解锁机器人中原子技能学习的潜力
AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots
AI总结：
AtomicVLA通过原子技能抽象和动态专家组合提升机器人长周期任务的性能
摘要：
最近在视觉-语言-动作（VLA）模型方面的进展显示了在机器人操作任务中的巨大潜力。然而，现实中的机器人任务往往涉及长周期、多步骤的问题解决，并需要为持续技能学习进行泛化，这超出了单一动作或技能的范围。这些挑战对现有VLA模型构成了重大障碍，因为这些模型使用单一动作解码器训练于聚合数据，导致可扩展性差。为了解决这些挑战，我们提出了AtomicVLA，一个统一的规划与执行框架，该框架联合生成任务级计划、原子技能抽象和细粒度动作。AtomicVLA通过技能引导的专家混合（SG-MoE）构建可扩展的原子技能库，其中每个专家专门掌握通用但精确的原子技能。此外，我们引入了灵活的路由编码器，可以自动分配专用的原子专家到新技能，实现持续学习。我们通过广泛的实验验证了我们的方法。在模拟中，AtomicVLA在LIBERO上比π₀高出2.4%，在LIBERO-LONG上高出10%，在CALVIN上比π₀和π₀.5在平均任务长度上分别高出0.22和0.25。此外，我们的AtomicVLA在现实中的长周期任务和持续学习中始终比基线高出18.3%和21%。这些结果突显了原子技能抽象和动态专家组合在长周期和终身机器人任务中的有效性。项目页面是https://zhanglk9.github.io/atomicvla-web/。
英文摘要：
Recent advances in Visual-Language-Action (VLA) models have shown promising potential for robotic manipulation tasks. However, real-world robotic tasks often involve long-horizon, multi-step problem-solving and require generalization for continual skill acquisition, extending beyond single actions or skills. These challenges present significant barriers for existing VLA models, which use monolithic action decoders trained on aggregated data, resulting in poor scalability. To address these challenges, we propose AtomicVLA, a unified planning-and-execution framework that jointly generates task-level plans, atomic skill abstractions, and fine-grained actions. AtomicVLA constructs a scalable atomic skill library through a Skill-Guided Mixture-of-Experts (SG-MoE), where each expert specializes in mastering generic yet precise atomic skills. Furthermore, we introduce a flexible routing encoder that automatically assigns dedicated atomic experts to new skills, enabling continual learning. We validate our approach through extensive experiments. In simulation, AtomicVLA outperforms $π_{0}$ by 2.4\% on LIBERO, 10\% on LIBERO-LONG, and outperforms $π_{0}$ and $π_{0.5}$ by 0.22 and 0.25 in average task length on CALVIN. Additionally, our AtomicVLA consistently surpasses baselines by 18.3\% and 21\% in real-world long-horizon tasks and continual learning. These results highlight the effectiveness of atomic skill abstraction and dynamic expert composition for long-horizon and lifelong robotic tasks. The project page is \href{https://zhanglk9.github.io/atomicvla-web/}{here}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
