LEMMA：学习语言条件下的多机器人操作
LEMMA: Learning Language-Conditioned Multi-Robot Manipulation
AI总结：
本文提出语言条件下多机器人操作基准 LEMMA，包含8类程序化任务及专家演示，并设计模块化分层规划基线，以解决任务分配和长时程操作中的强时间依赖挑战。
摘要：
复杂的操作任务通常需要具有互补能力的机器人进行协作。我们提出了一个面向语言条件下多机器人操作（LEMMA）的基准，重点研究在桌面场景中基于人类语言指令的任务分配和长时程物体操作。LEMMA 包含 8 类程序化生成的任务，复杂度各不相同，其中一些任务要求机器人使用工具并将工具传递给彼此。对于每个任务，我们提供 800 条专家演示和人类指令用于训练与评估。与现有基准相比，LEMMA 带来了更大的挑战，因为它要求系统识别每个机械臂的局限性并相应地分配子任务，同时还要处理每个任务中强时间依赖关系。为应对这些挑战，我们提出了一种模块化分层规划方法作为基线。我们的结果凸显了 LEMMA 在开发未来语言条件下多机器人系统方面的潜力。
英文摘要：
Complex manipulation tasks often require robots with complementary capabilities to collaborate. We introduce a benchmark for LanguagE-Conditioned Multi-robot MAnipulation (LEMMA) focused on task allocation and long-horizon object manipulation based on human language instructions in a tabletop setting. LEMMA features 8 types of procedurally generated tasks with varying degree of complexity, some of which require the robots to use tools and pass tools to each other. For each task, we provide 800 expert demonstrations and human instructions for training and evaluations. LEMMA poses greater challenges compared to existing benchmarks, as it requires the system to identify each manipulator's limitations and assign sub-tasks accordingly while also handling strong temporal dependencies in each task. To address these challenges, we propose a modular hierarchical planning approach as a baseline. Our results highlight the potential of LEMMA for developing future language-conditioned multi-robot systems.
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。
