  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.
