MESA: An Evaluation Framework for Compositional, Semantic, and Spatial Generalization in Robotics

MESA is an evaluation framework that precisely measures language‑conditioned policy generalization in robotics. It offers five evaluation suites that separate in‑distribution performance from four axes of generalization—spatial configuration, object instance, object category, and subtask composition—allowing researchers to pinpoint why policies fail. The authors also introduce MESA‑Gen, a task and demonstration generation pipeline, and release two benchmark suites: a single‑arm MESA and BiMESA for bimanual generalization, with baseline results showing current models achieve only about 50% success, indicating significant room for improvement.

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• diffusion
• robotics
• multimodal
• benchmarks