在MetaWorld ML10基准上评估模型无关元学习：机器人操控任务中的快速适配
Evaluating Model-Agnostic Meta-Learning on MetaWorld ML10 Benchmark: Fast Adaptation in Robotic Manipulation Tasks
AI总结：
本文在MetaWorld ML10机器人操控基准上评估结合TRPO的MAML算法，验证其少样本适配效果，分析其泛化差距与性能方差，指出该类方法的潜力与局限。
摘要：
元学习算法能够利用极少量数据快速适配新任务，这是现实机器人系统的一项关键能力。本文在包含10项不同机器人操控任务的挑战性测试集MetaWorld ML10基准上，评估了与信赖域策略优化（TRPO）结合的模型无关元学习（MAML）算法。我们实现并分析了MAML-TRPO学习通用初始化参数的能力，该初始化可推动模型在推物、拾取、抽屉操作等语义不同的操控行为上实现少样本适配。实验表明，MAML可实现有效的单样本适配，仅经过单次梯度更新就有明确性能提升，在训练任务上最终成功率达21.0%，在留出的测试任务上最终成功率达13.2%。但我们观察到元训练过程中会出现泛化差距：测试任务性能进入平台期时，训练任务性能仍在持续提升。任务层面的分析显示适配效果存在高方差，不同操控技能的成功率在0%到80%之间浮动。这些发现既凸显了基于梯度的元学习在多样化机器人操控场景中的应用潜力，也点明了其当前局限，同时为任务感知适配、结构化策略架构方向的后续研究提供了思路。
英文摘要：
Meta-learning algorithms enable rapid adaptation to new tasks with minimal data, a critical capability for real-world robotic systems. This paper evaluates Model-Agnostic Meta-Learning (MAML) combined with Trust Region Policy Optimization (TRPO) on the MetaWorld ML10 benchmark, a challenging suite of ten diverse robotic manipulation tasks. We implement and analyze MAML-TRPO's ability to learn a universal initialization that facilitates few-shot adaptation across semantically different manipulation behaviors including pushing, picking, and drawer manipulation. Our experiments demonstrate that MAML achieves effective one-shot adaptation with clear performance improvements after a single gradient update, reaching final success rates of 21.0% on training tasks and 13.2% on held-out test tasks. However, we observe a generalization gap that emerges during meta-training, where performance on test tasks plateaus while training task performance continues to improve. Task-level analysis reveals high variance in adaptation effectiveness, with success rates ranging from 0% to 80% across different manipulation skills. These findings highlight both the promise and current limitations of gradient-based meta-learning for diverse robotic manipulation, and suggest directions for future work in task-aware adaptation and structured policy architectures.
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