FLAME：面向机器人操作的一种联邦学习基准
FLAME: A Federated Learning Benchmark for Robotic Manipulation
AI总结：
本文提出首个面向机器人操作的联邦学习基准FLAME，包含超16万条多环境专家演示及训练评估框架，通过评估标准联邦学习算法揭示分布式策略学习的潜力与关键挑战。
摘要：
机器人操作领域的最新进展得益于跨多种环境收集的大规模数据集。传统上，在这些数据集上训练机器人操作策略以集中式方式进行，这引发了关于可扩展性、适应性和数据隐私的担忧。虽然联邦学习能够实现去中心化、保护隐私的训练，但其在机器人操作中的应用在很大程度上仍未得到探索。我们提出了FLAME（跨操作环境的联邦学习，Federated Learning Across Manipulation Environments），这是第一个为机器人操作中的联邦学习设计的基准。FLAME包括：（i）一组大规模数据集，包含超过160,000条专家演示，涵盖多个操作任务，并在一系列广泛的模拟环境中收集；（ii）一个用于联邦设置下机器人策略学习的训练与评估框架。我们在FLAME中评估了标准联邦学习算法，展示了它们在分布式策略学习中的潜力，并指出了关键挑战。我们的基准为可扩展、自适应且具有隐私意识的机器人学习奠定了基础。
英文摘要：
Recent progress in robotic manipulation has been fueled by large-scale datasets collected across diverse environments. Training robotic manipulation policies on these datasets is traditionally performed in a centralized manner, raising concerns regarding scalability, adaptability, and data privacy. While federated learning enables decentralized, privacy-preserving training, its application to robotic manipulation remains largely unexplored. We introduce FLAME (Federated Learning Across Manipulation Environments), the first benchmark designed for federated learning in robotic manipulation. FLAME consists of: (i) a set of large-scale datasets of over 160,000 expert demonstrations of multiple manipulation tasks, collected across a wide range of simulated environments; (ii) a training and evaluation framework for robotic policy learning in a federated setting. We evaluate standard federated learning algorithms in FLAME, showing their potential for distributed policy learning and highlighting key challenges. Our benchmark establishes a foundation for scalable, adaptive, and privacy-aware robotic learning.
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。
