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.

我们介绍了一个基于人类语言指令的桌面环境下，侧重于任务分配和长时间跨度物体操作的多机器人语言条件操作（LEMMA）基准。LEMMA 具有多种类型的过程生成任务，其复杂度不同，其中一些需要机器人使用工具并将工具传递给彼此。为每个任务提供 800 个专家示范和人类指令进行训练和评估。LEMMA 相对于现有基准提出了更大的挑战，因为它要求系统识别每个操纵器的限制，并相应地分配子任务，同时还要处理每个任务中的强时间依赖。为应对这些挑战，我们提出了一种基于模块化分层规划方法作为基线。我们的结果突显了 LEMMA 在开发未来语言条件多机器人系统方面的潜力。