超越多数：用于机器人操作的长尾模仿学习
Beyond the Majority: Long-tail Imitation Learning for Robotic Manipulation
AI总结：
本文提出APA方法，通过从数据丰富的头部任务迁移知识以提升机器人操作中数据稀缺的尾部任务性能。
摘要：
尽管通用机器人策略在通过模仿学习多样操作技能方面具有显著潜力，但其性能常受到训练演示的长尾分布的阻碍。在这样的数据上学习的策略，由于严重偏向于少数数据丰富的头部任务，当面对大量数据稀缺的尾部任务时，通常表现出较差的泛化能力。在本工作中，我们对政策学习中普遍存在长尾挑战进行了全面分析。我们的分析首先通过展示传统长尾学习策略（例如重新采样）在提高策略在尾部任务上的性能上的无效性开始。随后，我们揭示了这一失败的内在机制，发现尾部任务的数据稀缺性直接损害了策略的空间推理能力。为克服这一问题，我们引入了接近阶段增强（APA），一种简单而有效的方案，该方案将数据丰富的头部任务的知识转移到数据稀缺的尾部任务，而无需外部演示。在仿真和现实世界操作任务中的大量实验展示了APA的有效性。我们的代码和演示已公开在：https://mldxy.github.io/Project-VLA-long-tail/。
英文摘要：
While generalist robot policies hold significant promise for learning diverse manipulation skills through imitation, their performance is often hindered by the long-tail distribution of training demonstrations. Policies learned on such data, which is heavily skewed towards a few data-rich head tasks, frequently exhibit poor generalization when confronted with the vast number of data-scarce tail tasks. In this work, we conduct a comprehensive analysis of the pervasive long-tail challenge inherent in policy learning. Our analysis begins by demonstrating the inefficacy of conventional long-tail learning strategies (e.g., re-sampling) for improving the policy's performance on tail tasks. We then uncover the underlying mechanism for this failure, revealing that data scarcity on tail tasks directly impairs the policy's spatial reasoning capability. To overcome this, we introduce Approaching-Phase Augmentation (APA), a simple yet effective scheme that transfers knowledge from data-rich head tasks to data-scarce tail tasks without requiring external demonstrations. Extensive experiments in both simulation and real-world manipulation tasks demonstrate the effectiveness of APA. Our code and demos are publicly available at: https://mldxy.github.io/Project-VLA-long-tail/.
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。
